Movement detection script¶
In this script, we process the motion tracking and build a pipeline to automatically detect a movement event within a specific marker (e.g., wrist, head, ...)
These are then used to compare them with manual annotations and evaluate the performance of the algorithm.
Outline
- Preparation
- here we center the data to have (semi)control for individual motion of keypoints
- we add 0 to the data
- we calculate natural noise in articulators in no-movement trials to manipulate the measurement error of OpenPose
- we calculate percentile of wrist movement threshold (20 cm/s) and corresponding thresholds for other articulators
- Annotation
- we annotate movement events based on the dynamic thresholds (with added natural noise)
- we apply rule-based algorithm to merge events or delete 'fake' events
- prepare ELAN-like tiers
- Creation of ELAN files
- from df with annotations, we create ELAN-like files
Preparation¶
Folder settings¶
In [1]:
import os
import glob
import numpy as np
import pandas as pd
curfolder = os.getcwd()
print(curfolder)
# data are in curfolder/P0/P0/
datafolder = curfolder + '/TS_motiontracking'
print(datafolder)
folderstotrack = glob.glob(datafolder + '/mt_*')
# get rid of all 'centered'
folderstotrack = [x for x in folderstotrack if 'centered' not in x]
print(folderstotrack)
e:\FLESH_ContinuousBodilyEffort\TS_processing e:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking ['e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_0_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_18_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_19_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_1_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_20_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_21_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_22_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_23_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_24_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_25_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_26_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_2_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_36_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_37_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_38_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_39_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_3_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_40_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_41_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_42_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_43_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_44_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_4_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_5_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_6_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_7_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_8_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_tpose_0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_10_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_11_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_12_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_13_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_14_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_15_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_16_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_17_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_27_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_28_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_29_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_30_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_31_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_32_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_33_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_35_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_45_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_46_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_47_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_48_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_49_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_50_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_51_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_52_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_53_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_9_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_tpose_1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_0_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_10_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_11_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_12_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_13_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_14_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_15_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_16_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_17_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_18_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_1_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_2_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_38_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_39_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_3_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_40_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_41_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_43_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_44_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_45_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_46_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_47_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_48_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_49_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_4_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_50_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_51_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_52_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_5_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_67_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_68_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_69_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_6_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_70_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_71_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_72_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_73_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_74_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_75_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_76_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_77_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_78_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_79_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_7_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_80_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_81_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_82_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_83_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_84_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_85_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_86_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_87_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_88_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_89_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_8_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_90_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_91_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_9_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_100_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_101_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_102_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_103_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_104_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_105_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_106_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_107_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_108_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_109_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_110_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_111_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_112_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_113_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_19_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_20_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_21_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_22_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_23_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_24_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_25_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_26_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_27_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_28_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_29_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_30_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_31_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_32_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_33_p1.csv', 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'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_60_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_61_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_62_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_63_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_64_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_65_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_92_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_93_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_94_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_95_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_96_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_97_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_98_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_99_p1.csv']
Centering keypoints¶
With motion tracking, keypoints are moving as a consequence of other keypoints moving. For instance, I can jump from the ground and all the keypoints will move, despite only the knees being responsible for the movement per se.
To get to individual movements, we will center the keypoints in the following way:
- head region
- head relative to shoulder midpoint
- torso region
- shoulders relative to hip midpoint
- arm region
- elbow, wrist relative to shoulder midpoint
- lower body region
- knee, ankle, heel relative to hip midpoint
- hip relative to heel midpoint
In [11]:
import scipy.signal
for file in folderstotrack:
print('working on' + file)
# load in the file
df = pd.read_csv(file)
# get rid of collumns we will not need
df = df.loc[:, ~df.columns.str.contains('Nose')]
df = df.loc[:, ~df.columns.str.contains('Toe')]
df = df.loc[:, ~df.columns.str.contains('speed')]
df = df.loc[:, ~df.columns.str.contains('vert_vel')]
##### HEAD
# now we want all head coordinates center on Shoulder midpoint (we cant use Neck because Neck often moves with head)
# create ShoulderMid
df['ShoulderMid_x'] = (df['RShoulder_x'] + df['LShoulder_x']) / 2
df['ShoulderMid_y'] = (df['RShoulder_y'] + df['LShoulder_y']) / 2
df['ShoulderMid_z'] = (df['RShoulder_z'] + df['LShoulder_z']) / 2
# center head on ShoulderMid
df['Head_x_c'] = df['Head_x'] - df['ShoulderMid_x']
df['Head_y_c'] = df['Head_y'] - df['ShoulderMid_y']
df['Head_z_c'] = df['Head_z'] - df['ShoulderMid_z']
#### TORSO
# create HipMid
df['HipMid_y'] = (df['LHip_y'] + df['RHip_y']) / 2
df['HipMid_x'] = (df['LHip_x'] + df['RHip_x']) / 2
df['HipMid_z'] = (df['LHip_z'] + df['RHip_z']) / 2
# center shoulders on HipMid
df['LShoulder_x_c'] = df['LShoulder_x'] - df['HipMid_x']
df['LShoulder_y_c'] = df['LShoulder_y'] - df['HipMid_y']
df['LShoulder_z_c'] = df['LShoulder_z'] - df['HipMid_z']
df['RShoulder_x_c'] = df['RShoulder_x'] - df['HipMid_x']
df['RShoulder_y_c'] = df['RShoulder_y'] - df['HipMid_y']
df['RShoulder_z_c'] = df['RShoulder_z'] - df['HipMid_z']
#### ARMS
# center arm keypoints on ShoulderMid
df['LElbow_x_c'] = df['LElbow_x'] - df['ShoulderMid_x']
df['LElbow_y_c'] = df['LElbow_y'] - df['ShoulderMid_y']
df['LElbow_z_c'] = df['LElbow_z'] - df['ShoulderMid_z']
df['RElbow_x_c'] = df['RElbow_x'] - df['ShoulderMid_x']
df['RElbow_y_c'] = df['RElbow_y'] - df['ShoulderMid_y']
df['RElbow_z_c'] = df['RElbow_z'] - df['ShoulderMid_z']
df['LWrist_x_c'] = df['LWrist_x'] - df['ShoulderMid_x']
df['LWrist_y_c'] = df['LWrist_y'] - df['ShoulderMid_y']
df['LWrist_z_c'] = df['LWrist_z'] - df['ShoulderMid_z']
df['RWrist_x_c'] = df['RWrist_x'] - df['ShoulderMid_x']
df['RWrist_y_c'] = df['RWrist_y'] - df['ShoulderMid_y']
df['RWrist_z_c'] = df['RWrist_z'] - df['ShoulderMid_z']
#### LOWER BODX
# center lower body keypoits on HipMid
df['LKnee_x_c'] = df['LKnee_x'] - df['HipMid_x']
df['LKnee_y_c'] = df['LKnee_y'] - df['HipMid_y']
df['LKnee_z_c'] = df['LKnee_z'] - df['HipMid_z']
df['RKnee_x_c'] = df['RKnee_x'] - df['HipMid_x']
df['RKnee_y_c'] = df['RKnee_y'] - df['HipMid_y']
df['RKnee_z_c'] = df['RKnee_z'] - df['HipMid_z']
df['LHeel_x_c'] = df['LHeel_x'] #- df['HipMid_x']
df['LHeel_y_c'] = df['LHeel_y'] #- df['HipMid_y']
df['LHeel_z_c'] = df['LHeel_z'] #- df['HipMid_z']
df['RHeel_x_c'] = df['RHeel_x'] #- df['HipMid_x']
df['RHeel_y_c'] = df['RHeel_y'] #- df['HipMid_y']
df['RHeel_z_c'] = df['RHeel_z'] #- df['HipMid_z']
# also center hip on HeelMid
# create HeelMid
df['HeelMid_y'] = (df['LHeel_y'] + df['RHeel_y']) / 2
df['HeelMid_x'] = (df['LHeel_x'] + df['RHeel_x']) / 2
df['HeelMid_z'] = (df['LHeel_z'] + df['RHeel_z']) / 2
# now we want to center hip on HeelMiD
df['LHip_x_c'] = df['LHip_x'] - df['HeelMid_x']
df['LHip_y_c'] = df['LHip_y'] - df['HeelMid_y']
df['LHip_z_c'] = df['LHip_z'] - df['HeelMid_z']
df['RHip_x_c'] = df['RHip_x'] - df['HeelMid_x']
df['RHip_y_c'] = df['RHip_y'] - df['HeelMid_y']
df['RHip_z_c'] = df['RHip_z'] - df['HeelMid_z']
# keep only the centered columns and Time, TrialD
df = df.loc[:, df.columns.str.contains('_c') | df.columns.str.contains('Time') | df.columns.str.contains('TrialID')]
# collect all columns except Time and TrialD
mtcols = list(df.columns)
mtcols.remove('Time')
mtcols.remove('TrialID')
# get Time back to sec
df['Time'] = df['Time']/1000
# get sampling rate
sr = 1/np.mean(np.diff(df['Time']))
# prepare cols for speed
cols = [x.split('_')[0] for x in mtcols]
colsforspeed = list(set(cols))
# upper body cols
upperbodycols = ['Head', 'Neck', 'RShoulder', 'RElbow', 'RWrist', 'LShoulder', 'LElbow', 'LWrist']
# lower body cols
lowerbodycols = ['RHip', 'RKnee', 'RAnkle', 'RHeel' 'LHip', 'LKnee', 'LAnkle', 'LHeel']
# for each unique column, calculate speed
for col in colsforspeed:
# get x and y columns
x = df[col + '_x_c']
y = df[col + '_y_c']
z = df[col + '_z_c'] # note that y and z are flipped
# calculate speed
df[col + '_speed'] = np.insert(np.sqrt(np.diff(x)**2 + np.diff(y)**2 + np.diff(z)**2), 0, 0)
# multiply the values by sr, because now we have values in m/(s/sr), and by 1000 to get ms
df[col + '_speed'] = df[col + '_speed']*sr
# smooth with savgol
if any(x in col for x in upperbodycols):
df[col + '_speed'] = scipy.signal.savgol_filter(df[col + '_speed'], 15, 1)
elif any(x in col for x in lowerbodycols):
df[col + '_speed'] = scipy.signal.savgol_filter(df[col + '_speed'], 40, 1) # there is a lot of noise in lower body, so we increase the window size
# if the col contains wrist, we will alco calculate the vertical velocity (z dimension)
if 'Wrist' in col:
# calculate speed
df[col + '_vert_vel'] = np.insert(np.diff(z), 0, 0)
# multiply the values by sr, because now we have values in m/(s/sr)
df[col + '_vert_vel'] = df[col + '_vert_vel']*sr
# smooth with savgol
df[col + '_vert_vel'] = scipy.signal.savgol_filter(df[col + '_vert_vel'], 15, 1)
# get time back to ms
df['Time'] = df['Time']*1000
# save df as a new file
df.to_csv(file.replace('mt_', 'mt_centered_'), index=False)
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In [12]:
# plot all speeds for RKnee in df
import matplotlib.pyplot as plt
import seaborn as sns
sample = df
# get all columns with RKnee
cols = [x for x in sample.columns if 'RKnee_speed' in x]
# plot all columns
for col in cols:
plt.plot(sample['Time'], sample[col], label=col)
plt.legend()
plt.show()
Natural noise of articulators¶
Due to motion tracking error, often it is the case that there is a movement noise in the keypoints that seldomly move, while they stay (relatively) motionless. To account for this, before calculating threshold of movement of each keypoint, we will calculate what is the natural noise of keypoints in trials where there is no movement (except hands, which we will ignore)
No-movement trials (excl. arms)¶
In [15]:
# get centered files
nomovefiles = glob.glob(datafolder + '/mt_centered_0_1_*')
# there are files without motion of the body (excl. arms)
foi = ['0_1_p', '_0_p', '_2_p', '_3_p', '_5_p', '_6_p', '_7_p', '_8_p', '_9_p', '_11_p', '_12_p', '_13_p', '_14_p', '_15_p', '_16_p']
noisefiles = [x for x in nomovefiles if any(y in x for y in foi)]
print(noisefiles)
# columns with motionless points
coi = ['Head', 'RHip', 'RKnee', 'RHeel', 'LHip', 'LKnee', 'LHeel']
# for each file, calculate the mean speed, max speed, min speed, sd speed for each column in coi for speed
noise = {}
# concatenate all the files together to one df
allnoise = pd.concat([pd.read_csv(file) for file in noisefiles])
# get all columns with speed
cols = [x for x in allnoise.columns if 'speed' in x]
# keep only those that are in coi
cols = [x for x in cols if any(y in x for y in coi)]
# for each column, calculate the mean, max, min, sd
for col in cols:
print(col)
# if the col starts on R or L, we want to concatenate the other side (left or right) and calculate the mean etc. on both together
if 'R' in col:
othercol = col.replace('R', 'L')
name = col.replace('R', '')
# get the values for both columns
keytocount = allnoise[col] + allnoise[othercol]
# get the mean
print('mean: ' + str(np.mean(keytocount)))
print('max: ' + str(np.max(keytocount)))
print('min: ' + str(np.min(keytocount)))
print('sd: ' + str(np.std(keytocount)))
# save the values
values = [np.mean(keytocount), np.max(keytocount), np.min(keytocount), np.std(keytocount)]
noise[name] = values
elif 'L' in col:
# we already calculated it so we can skip it
print('skipping ' + col)
else:
name = col
# get the values for both columns
keytocount = allnoise[col]
# get the mean
print('mean: ' + str(np.mean(keytocount)))
print('max: ' + str(np.max(keytocount)))
print('min: ' + str(np.min(keytocount)))
print('sd: ' + str(np.std(keytocount)))
# save the values
values = [np.mean(keytocount), np.max(keytocount), np.min(keytocount), np.std(keytocount)]
noise[name] = values
['e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_0_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_11_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_12_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_13_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_14_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_15_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_16_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_2_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_3_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_5_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_6_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_7_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_8_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_9_p1.csv'] LKnee_speed skipping LKnee_speed LHeel_speed skipping LHeel_speed RHeel_speed mean: 5.004875624374936 max: 16.954267438838876 min: 0.5106655303723385 sd: 2.7607815463614553 RHip_speed mean: 10.326424849596721 max: 54.49077758547286 min: 0.9239982664543706 sd: 9.017960876402839 Head_speed mean: 4.3243746575386215 max: 18.32696145797376 min: -0.7498397719774741 sd: 2.719665513018643 LHip_speed skipping LHip_speed RKnee_speed mean: 8.812762536028025 max: 33.385013262411306 min: 2.4257931690567514 sd: 4.36462620292907
No-movement trials (arms)¶
In [131]:
# get centered files
nomovefiles = glob.glob(datafolder + '/mt_centered_0_1_*')
# this is file without no motion completely
foi = ['_1_p']
noisefiles = [x for x in nomovefiles if any(y in x for y in foi)]
# columns with motionless points
coi = ['RWrist', 'LWrist', 'RShoulder', 'LShoulder', 'RElbow', 'LElbow']
# for each file, calculate the mean speed, max speed, min speed, sd speed for each column in coi for speed
wnoise = {}
# concatenate all the files together to one df
allnoise = pd.concat([pd.read_csv(file) for file in noisefiles])
# get all columns with speed
cols = [x for x in allnoise.columns if 'speed' in x or 'vert_vel' in x]
# keep only those that are in coi
cols = [x for x in cols if any(y in x for y in coi)]
# for each column, calculate the mean, max, min, sd
for col in cols:
print(col)
# if the col starts on R or L, we want to concatenate the other side (left or right) and calculate the mean etc. on both together
if 'R' in col:
othercol = col.replace('R', 'L')
name = col.replace('R', '')
# get the values for both columns
keytocount = allnoise[col] + allnoise[othercol]
# get the mean
print('mean: ' + str(np.mean(keytocount)))
print('max: ' + str(np.max(keytocount)))
print('min: ' + str(np.min(keytocount)))
print('sd: ' + str(np.std(keytocount)))
# save the values
values = [np.mean(keytocount), np.max(keytocount), np.min(keytocount), np.std(keytocount)]
wnoise[name] = values
elif 'L' in col:
# we already calculated it so we can skipit
print('skipping ' + col)
RWrist_speed mean: 8.148664198131078 max: 11.740306021401924 min: 4.618764556306746 sd: 1.9990770440761167 RWrist_vert_vel mean: 0.3149308813912007 max: 3.614311484827629 min: -5.6034372531266925 sd: 2.0154133461692703 LElbow_speed skipping LElbow_speed RShoulder_speed mean: 10.854913809687398 max: 19.465255916522874 min: 5.630889362142506 sd: 3.858362757020072 RElbow_speed mean: 7.5279665259728095 max: 12.248205125018949 min: 3.878283273187666 sd: 2.1896929375912517 LWrist_speed skipping LWrist_speed LWrist_vert_vel skipping LWrist_vert_vel LShoulder_speed skipping LShoulder_speed
For reaching to grasp, it is not uncommon to take a speed threshold of 15cm/s as a movement of the hand (e.g., based on wrist movement translation). We should be aware however, that while such values make sense for a particular articulator, other articulators may be less mobile, and are judged to move much earlier relative to background noise micro movements. As such, we devised an 'empirical threshold' applied for the whole data, by calculating the percentile of the distributions of wrist speeds at 15cm/s, and then using that percentile to get the thresholds for the other articulators based on their observed speed distributions. The distributions were consistently long-tailed so we constructed log-transformed distributions. Supplemental figure S1, shows all the thresholds we determined with this emprical threshold procedure. (Wim)
Calculate threshold for movement as percentage¶
In [42]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
wrist_speed = []
# get wrist speeds for all files
for file in centeredfiles:
df = pd.read_csv(file)
wrist_speed.extend(df['RWrist_speed'].tolist())
wrist_speed.extend(df['LWrist_speed'].tolist())
wrist_speed = np.array(wrist_speed)
# get rid of outliers based on Tukeys rule
Q1 = np.percentile(wrist_speed, 25)
Q3 = np.percentile(wrist_speed, 75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
wrist_speed = wrist_speed[(wrist_speed > lower_bound) & (wrist_speed < upper_bound)]
# what is the max value of the speed for wrists
# speed_max = df[['RWrist_speed', 'LWrist_speed']].max().max()
speed_15cm_percentage = 15 / wrist_speed.max() * 100
# what is 15 cm/s in %
#speed_15cm_percentage = 15 / speed_max * 100
print('15 cm/s is in % ' + str(speed_15cm_percentage))
# store the thresholds for other keypoints
dynamic_thresholds = {}
# left and right should be treated as one
combined_keypoints = [
('LElbow_speed', 'RElbow_speed'),
('LShoulder_speed', 'RShoulder_speed'),
('LKnee_speed', 'RKnee_speed'),
('LHip_speed', 'RHip_speed'),
('LAnkle_speed', 'RAnkle_speed'),
('LHeel_speed', 'RHeel_speed'),
('LBigToe_speed', 'RBigToe_speed'),
('LSmallToe_speed', 'RSmallToe_speed')
]
for keypoint_pair in combined_keypoints:
joint_speed = []
for file in folderstotrack:
df = pd.read_csv(file)
joint_speed.extend(df[keypoint_pair[0]].tolist())
joint_speed.extend(df[keypoint_pair[1]].tolist())
joint_speed = np.array(joint_speed)
dynamic_threshold = (speed_15cm_percentage / 100) * joint_speed.max()
dynamic_thresholds[keypoint_pair] = dynamic_threshold
# collect remaining keypoints
speed_columns = [col for col in df.columns if 'speed' in col and all(key not in col for key in ['Wrist', 'Elbow', 'Shoulder', 'Knee', 'Hip', 'Ankle', 'Heel', 'BigToe', 'SmallToe'])
]
for keypoint in speed_columns:
keypoint_speed = []
for file in folderstotrack:
df = pd.read_csv(file)
keypoint_speed.extend(df[keypoint].tolist())
keypoint_speed = np.array(keypoint_speed)
dynamic_threshold = (speed_15cm_percentage / 100) * keypoint_speed.max()
dynamic_thresholds[keypoint] = dynamic_threshold
for key, threshold in dynamic_thresholds.items():
print(f'{key} threshold: {threshold}')
15 cm/s is in % 14.838139997397404
('LElbow_speed', 'RElbow_speed') threshold: 29.41884905078313
('LShoulder_speed', 'RShoulder_speed') threshold: 14.464364340601986
('LKnee_speed', 'RKnee_speed') threshold: 15.45234607970934
('LHip_speed', 'RHip_speed') threshold: 9.819380891940563
('LAnkle_speed', 'RAnkle_speed') threshold: 21.405892858237596
('LHeel_speed', 'RHeel_speed') threshold: 24.101081874231696
('LBigToe_speed', 'RBigToe_speed') threshold: 19.197425758475088
('LSmallToe_speed', 'RSmallToe_speed') threshold: 20.086653993973112
Head_speed threshold: 15.587675175509778
Neck_speed threshold: 17.35276641179739
Nose_speed threshold: 15.981094566112416
Note that taking percentage is sensitive to extreme values in the distribution. Therefore, in the next step we take percentile instead
Calculate threshold for movement as percentile¶
In [17]:
# load in one file
centeredfiles = glob.glob(datafolder + '/mt_centered_*')
sample = pd.read_csv(centeredfiles[0])
# get all the keypoint columns
keypoint_columns = [col for col in sample.columns if 'speed' in col]
# for each keypoint column, prepare empty list to store later the logged values
for keypoint in keypoint_columns:
# create a list that has name of the keypoint
globals()[keypoint] = []
all_keypoints = {}
for file in centeredfiles:
print('working on' + file)
df = pd.read_csv(file)
for keypoint in keypoint_columns:
# log the values
globals()[keypoint].extend(np.log(df[keypoint].tolist()))
all_keypoints[keypoint] = globals()[keypoint]
# Define combined keypoints pairs
combined_keypoints = [
('LElbow_speed', 'RElbow_speed'),
('LShoulder_speed', 'RShoulder_speed'),
('LKnee_speed', 'RKnee_speed'),
('LHip_speed', 'RHip_speed'),
('LHeel_speed', 'RHeel_speed'),
('LWrist_speed', 'RWrist_speed')
]
for keypoint_pair in combined_keypoints:
left_keypoint = keypoint_pair[0]
right_keypoint = keypoint_pair[1]
# combine logged values
combined_logged = np.concatenate([all_keypoints[left_keypoint], all_keypoints[right_keypoint]])
# store the combined values in the dictionary
all_keypoints[keypoint_pair] = combined_logged
# get percentile of 20 cm/s in RWrist_speed
# get the tuple LWrist_speed and RWrist_speed
wrist_speed = all_keypoints[('LWrist_speed', 'RWrist_speed')]
# sort the RWrist_speed
wspeed_sorted = np.sort(wrist_speed)
# get the rank of 15 cm/s
threshold = np.log(20)
rank = np.searchsorted(wspeed_sorted, threshold, side='right')
percentile = rank / len(wrist_speed) *100
print('20 cm/s is in % ' + str(percentile))
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_0_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_10_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_11_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_12_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_13_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_14_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_15_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_16_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_17_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_18_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_19_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_1_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_20_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_21_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_22_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_23_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_24_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_25_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_26_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_27_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_28_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_29_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_2_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_30_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_31_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_32_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_33_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_35_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_36_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_37_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_38_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_39_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_3_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_40_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_41_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_42_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_43_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_44_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_45_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_46_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_47_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_48_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_49_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_4_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_50_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_51_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_52_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_53_p1.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist()))
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_5_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_6_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_7_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_8_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_9_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_tpose_0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_tpose_1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_0_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_100_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_101_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_102_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_103_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_104_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_105_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_106_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_107_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_108_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_109_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_10_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_110_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_111_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_112_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_113_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_11_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_12_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_13_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_14_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_15_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_16_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_17_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_18_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_19_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_1_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_20_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_21_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_22_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_23_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_24_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_25_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_26_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_27_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_28_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_29_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_2_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_30_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_31_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_32_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_33_p1.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist()))
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_34_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_35_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_36_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_37_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_38_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_39_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_3_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_40_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_41_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_43_p0.csv 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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_53_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_54_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_55_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_56_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_57_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_58_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_59_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_5_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_60_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_61_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_62_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_63_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_64_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_65_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_67_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_68_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_69_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_6_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_70_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_71_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_72_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_73_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_74_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_75_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_76_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_77_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_78_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_79_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_7_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_80_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_81_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_82_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_83_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_84_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_85_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_86_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_87_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_88_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_89_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_8_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_90_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_91_p0.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_92_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_93_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_94_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_95_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_96_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_97_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_98_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_99_p1.csv working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_9_p0.csv 20 cm/s is in % 52.351122354736376
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist())) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log globals()[keypoint].extend(np.log(df[keypoint].tolist()))
In [18]:
# store the thresholds for other keypoints
dynamic_thresholds = {}
dynamic_thresholds_log = {}
for keypoint_pair in combined_keypoints:
# access the lists associated to the keypoint pair
array1 = all_keypoints[keypoint_pair[0]]
array2 = all_keypoints[keypoint_pair[1]]
# convert into numpy array
array1 = np.array(array1)
array2 = np.array(array2)
# combine the two arrays
joint_logged = np.concatenate([array1, array2])
# check for NaN values
if np.isnan(joint_logged).any():
# handle NaN values
joint_logged = joint_logged[~np.isnan(joint_logged)]
# calculate the percentile
dynamic_threshold_log = np.percentile(joint_logged, percentile)
dynamic_threshold = np.exp(dynamic_threshold_log)
# store the threshold
dynamic_thresholds[keypoint_pair] = dynamic_threshold
dynamic_thresholds_log[keypoint_pair] = dynamic_threshold_log
# create list of keypoint columns that are not in combined_keypoints
speed_columns = [col for col in df.columns if 'speed' in col and all(key not in col for key in ['Wrist', 'Elbow', 'Shoulder', 'Knee', 'Hip', 'Ankle', 'Heel'])
]
# loop over the remaining keypoints lists and find the value at this percentage
for keypoint in speed_columns:
print(keypoint)
# convert to array
array = np.array(globals()[keypoint])
# Check for NaN values
if np.isnan(array).any():
# Handle NaN values
array = array[~np.isnan(array)]
# check for inf values and -inf
if np.isinf(array).any():
# Handle inf values
array = array[~np.isinf(array)]
dynamic_threshold_log = np.percentile(array, percentile)
print(dynamic_threshold_log)
dynamic_threshold = np.exp(dynamic_threshold_log)
print(dynamic_threshold)
dynamic_thresholds[keypoint] = dynamic_threshold
dynamic_thresholds_log[keypoint] = dynamic_threshold_log
for key, threshold in dynamic_thresholds.items():
print(f'{key} threshold: {threshold}')
for key, threshold in dynamic_thresholds_log.items():
print(f'{key} threshold: {threshold}')
Head_speed
2.0238553748054136
7.567444098264241
('LElbow_speed', 'RElbow_speed') threshold: 11.277574378580344
('LShoulder_speed', 'RShoulder_speed') threshold: 8.550446304012498
('LKnee_speed', 'RKnee_speed') threshold: 6.535496954264708
('LHip_speed', 'RHip_speed') threshold: 7.408189975161015
('LHeel_speed', 'RHeel_speed') threshold: 3.6409926065783074
('LWrist_speed', 'RWrist_speed') threshold: 19.970355651487136
Head_speed threshold: 7.567444098264241
('LElbow_speed', 'RElbow_speed') threshold: 2.422816185586166
('LShoulder_speed', 'RShoulder_speed') threshold: 2.14598348088604
('LKnee_speed', 'RKnee_speed') threshold: 1.8772483892139382
('LHip_speed', 'RHip_speed') threshold: 2.0025861416102306
('LHeel_speed', 'RHeel_speed') threshold: 1.2922563385892867
('LWrist_speed', 'RWrist_speed') threshold: 2.9942489565574295
Head_speed threshold: 2.0238553748054136
Plot the thresholds for movement¶
In [19]:
import matplotlib.pyplot as plt
# Define the number of subplots (one for each keypoint)
num_keypoints = len(dynamic_thresholds)
num_cols = 2 # Number of columns in the subplot grid
num_rows = (num_keypoints + num_cols - 1) // num_cols # Number of rows needed
fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 5 * num_rows))
# Flatten the axes array for easy iteration if it is multidimensional
if num_rows > 1:
axes = axes.flatten()
# Loop over keys in dynamic_thresholds
for i, keypoint in enumerate(dynamic_thresholds.keys()):
logged_values = all_keypoints[keypoint]
print(keypoint)
print(logged_values)
# if there any inf values skip them
if np.isinf(logged_values).any():
# Handle inf values
logged_values = logged_values[~np.isinf(logged_values)]
# Plot the histogram of the logged values
axes[i].hist(logged_values, bins=100, density=True)
axes[i].set_title(keypoint)
# Determine which dynamic threshold to use based on keypoint type
if isinstance(keypoint, tuple):
# Combined keypoints
threshold_log = dynamic_thresholds_log[keypoint]
threshold = dynamic_thresholds[keypoint]
else:
# Individual keypoints
threshold_log = dynamic_thresholds_log[keypoint]
threshold = dynamic_thresholds[keypoint]
# Add a vertical line for the threshold (logged value)
axes[i].axvline(threshold_log, color='r')
axes[i].text(threshold_log, 0.20, f'{threshold_log:.2f}', rotation=0, va='bottom', ha='center') # Label in log
# Calculate the corresponding unlogged threshold value
threshold_unlog = np.exp(threshold_log)
axes[i].text(np.log(threshold_unlog), 0.15, f'{threshold_unlog:.2f}', rotation=0, va='center', ha='center') # Label in unlog
# Get the current axis
ax = axes[i]
# Get the current tick positions on the x-axis
x_ticks = ax.get_xticks()
# Compute the corresponding unlogged values
unlogged_values = np.exp(x_ticks)
# Format the tick labels to include both logged and unlogged values
x_labels = [f'{log_val:.2f} ({unlog_val:.2f})' for log_val, unlog_val in zip(x_ticks, unlogged_values)]
# Set the new tick labels
ax.set_xticklabels(x_labels)
# Rotate x labels
ax.tick_params(axis='x', rotation=45)
# Set the labels for the x-axis
ax.set_xlabel('Logged values (Unlogged values)')
# Remove any unused subplots
for j in range(i + 1, len(axes)):
fig.delaxes(axes[j])
plt.tight_layout()
plt.show()
('LElbow_speed', 'RElbow_speed')
[3.01940643 3.01940643 3.00330792 ... 1.83128637 1.84566428 1.85983839]
('LShoulder_speed', 'RShoulder_speed')
[2.28757072 2.28757072 2.3131217 ... 1.28922951 1.27587209 1.26233382]
('LKnee_speed', 'RKnee_speed')
[2.06147757 2.06147757 2.04878219 ... nan nan nan]
('LHip_speed', 'RHip_speed')
[ -inf -inf 3.29334545 ... 0.47488421 0.24305145
-0.05928257]
('LHeel_speed', 'RHeel_speed')
[0.99208283 0.99208283 0.99668797 ... 0.95888441 0.95888441 0.95888441]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. ax.set_xticklabels(x_labels) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. ax.set_xticklabels(x_labels) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. ax.set_xticklabels(x_labels) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. ax.set_xticklabels(x_labels) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. ax.set_xticklabels(x_labels) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. ax.set_xticklabels(x_labels) C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. ax.set_xticklabels(x_labels)
('LWrist_speed', 'RWrist_speed')
[ 4.00823891 4.00823891 4.00164051 ... 0.67081348 0.23961315
-0.53496938]
Head_speed
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Annotate movement based on thresholds¶
Note that y and z dimensions are swapped (such that z is vertical)
In [20]:
def determine_movement(value, threshold):
if value > threshold:
return 1
else:
return 0
In [130]:
wnoise
Out[130]:
{'Wrist_speed': [8.148664198131078,
11.740306021401924,
4.618764556306746,
1.9990770440761167],
'Wrist_vert_vel': [0.3149308813912007,
3.614311484827629,
-5.6034372531266925,
2.0154133461692703],
'Shoulder_speed': [10.854913809687398,
19.465255916522874,
5.630889362142506,
3.858362757020072]}
In [21]:
annofolder = curfolder + '/MT_annotated'
Annotate events¶
In [298]:
noise
Out[298]:
{'Heel_speed': [5.004875624374936,
16.954267438838876,
0.5106655303723385,
2.7607815463614553],
'Hip_speed': [10.326424849596721,
54.49077758547286,
0.9239982664543706,
9.017960876402839],
'Head_speed': [4.3243746575386215,
18.32696145797376,
-0.7498397719774741,
2.719665513018643],
'Knee_speed': [8.812762536028025,
33.385013262411306,
2.4257931690567514,
4.36462620292907]}
In [299]:
import pandas as pd
import numpy as np
for file in centeredfiles:
print('working on' + file)
# last element is trialid
trialid = file.split('\\')[-1]
trialid = trialid.split('.')[0]
# load it
mt = pd.read_csv(file)
# get the sampling rate
sr = 1 / (mt['Time'].diff().mean())
# show columns
cols = mt.columns
# put away 'Time' column from the list
cols = [x for x in cols if x != 'Time']
cols = [x for x in cols if x != 'TrialID']
vvcols = [x for x in cols if 'vert_vel' in x]
# keep only those with speed
speedcols = [x for x in cols if 'speed' in x]
# for each unique colname (cols), get the threshold for movement
for col in speedcols:
# annotate movement
mt[col + '_movement'] = None
# if the col starts with R or L, then we will find the threshold in a tuple
if col.startswith('R'):
threshold = dynamic_thresholds[(col.replace('R', 'L'), col)]
# lets treat keypoints differently, as they are differently affected by noise, but mostly, we add some noise (+ sd) to the calculated dynamic threshold
if 'Wrist' in col:
threshold = threshold + wnoise[col.replace('R', '')][0] + wnoise[col.replace('R', '')][3]*2 # threshold + mean noise + sd noise
elif 'Elbow' in col:
threshold = threshold + wnoise[col.replace('R', '')][0] + wnoise[col.replace('R', '')][3]*2
print(threshold)
elif 'Knee' in col:
threshold = threshold + noise[col.replace('R', '')][0] #- noise[col.replace('R', '')][3] # here we add only mean noise as it seems that sd would smooth too much meaningful movement away
elif 'Shoulder' in col:
threshold = threshold + wnoise[col.replace('R', '')][0] - wnoise[col.replace('R', '')][3]
elif 'Hip' in col:
threshold = threshold + noise[col.replace('R', '')][0] + noise[col.replace('R', '')][3]*2
elif 'Heel' in col:
threshold = threshold + noise[col.replace('R', '')][0] + noise[col.replace('R', '')][3]*2
else:
if 'Elbow' in col: # we ignore Elbow because we are fine with wrist to annotate arm movement
continue
else:
threshold = threshold + noise[col.replace('R', '')][0] + noise[col.replace('R', '')][3]
elif col.startswith('L'):
threshold = dynamic_thresholds[(col, col.replace('L', 'R'))]
if 'Wrist' in col:
threshold = threshold + wnoise[col.replace('L', '')][0] + wnoise[col.replace('L', '')][3]*2
elif 'Elbow' in col:
threshold = threshold + wnoise[col.replace('L', '')][0] + wnoise[col.replace('L', '')][3]*2
elif 'Knee' in col:
threshold = threshold + noise[col.replace('L', '')][0] #- noise[col.replace('L', '')][3]
elif 'Shoulder' in col:
threshold = threshold + wnoise[col.replace('L', '')][0] - wnoise[col.replace('L', '')][3]
elif 'Hip' in col:
threshold = threshold + noise[col.replace('L', '')][0] + noise[col.replace('L', '')][3]*2
elif 'Heel' in col:
threshold = threshold + noise[col.replace('L', '')][0] + noise[col.replace('L', '')][3]*2
else:
threshold = threshold + noise[col.replace('L', '')][0] + noise[col.replace('L', '')][3]
else:
threshold = dynamic_thresholds[col]
if 'Head' in col:
threshold = threshold + noise[col][0] + noise[col][3]*2
else:
threshold = threshold + noise[col][0] + noise[col][3]
# determine movement
mt[col + '_movement'] = mt[col].apply(lambda x: determine_movement(x, threshold))
# annotate events
mt[col + '_event'] = (mt[col + '_movement'] != mt[col + '_movement'].shift(1)) & (mt[col + '_movement'] == 1)
mt[col + '_event'] = mt[col + '_event'].cumsum()
# if there is no movement, then event is 0
mt.loc[mt[col + '_movement'] == 0, col + '_event'] = 0
for col in vvcols:
# for each of these columns, create a col_movement column
mt[col + '_movement'] = None
# threshold
if col.startswith('R'):
vvel_threshold = 20 + wnoise[col.replace('R', '')][0] + wnoise[col.replace('R', '')][3] # in the velocity, the noise is not so high
elif col.startswith('L'):
vvel_threshold = 20 + wnoise[col.replace('L', '')][0] + wnoise[col.replace('L', '')][3]
# if the speed in col_speed is smaller than -15 or bigger than 15, then movement is 1, else 0
mt.loc[(mt[col] >= vvel_threshold) | (mt[col] <= -vvel_threshold), col + '_movement'] = 1
mt.loc[(mt[col] < vvel_threshold) & (mt[col] > -vvel_threshold), col + '_movement'] = 0
# annotate events
mt[col + '_movement_event'] = (mt[col + '_movement'] != mt[col + '_movement'].shift(1)) & (mt[col + '_movement'] == 1)
mt[col + '_movement_event'] = mt[col + '_movement_event'].cumsum()
# if there is no movement, then event is 0
mt.loc[mt[col + '_movement'] == 0, col + '_movement_event'] = 0
# get rid of all columns with _y, _x, _z
mt = mt[[x for x in mt.columns if not any(y in x for y in ['_x', '_y', '_z'])]]
# save the annotated file
mt.to_csv(annofolder + '/' + trialid + '_annotated.csv', index=False)
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_0_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_10_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_11_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_12_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_13_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_14_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_15_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_16_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_17_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_18_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_19_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_1_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_20_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_21_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_22_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_23_p0.csv 23.184926779735658 working 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one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_3_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_40_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_41_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_42_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_43_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_44_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_45_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_46_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_47_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_48_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_49_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_4_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_50_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_51_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_52_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_53_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_5_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_6_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_7_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_8_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_9_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_tpose_0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_tpose_1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_0_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_100_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_101_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_102_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_103_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_104_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_105_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_106_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_107_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_108_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_109_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_10_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_110_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_111_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_112_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_113_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_11_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_12_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_13_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_14_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_15_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_16_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_17_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_18_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_19_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_1_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_20_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_21_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_22_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_23_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_24_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_25_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_26_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_27_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_28_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_29_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_2_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_30_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_31_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_32_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_33_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_34_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_35_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_36_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_37_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_38_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_39_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_3_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_40_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_41_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_43_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_44_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_45_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_46_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_47_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_48_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_49_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_4_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_50_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_51_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_52_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_53_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_54_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_55_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_56_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_57_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_58_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_59_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_5_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_60_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_61_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_62_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_63_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_64_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_65_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_67_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_68_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_69_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_6_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_70_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_71_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_72_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_73_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_74_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_75_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_76_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_77_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_78_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_79_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_7_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_80_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_81_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_82_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_83_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_84_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_85_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_86_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_87_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_88_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_89_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_8_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_90_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_91_p0.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_92_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_93_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_94_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_95_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_96_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_97_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_98_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_99_p1.csv 23.184926779735658 working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_9_p0.csv 23.184926779735658
In [208]:
annofiles = glob.glob(annofolder + '/*.csv')
# skip those files that have ELAN in the name
annofiles = [x for x in annofiles if 'ELAN' not in x]
# keep only centered in
annofiles = [x for x in annofiles if 'centered' in x]
print(annofiles)
['e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_0_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_10_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_11_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_12_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_13_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_14_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_15_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_16_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_17_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_18_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_19_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_1_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_20_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_21_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_22_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_23_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_24_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_25_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_26_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_27_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_28_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_29_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_2_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_30_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_31_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_32_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_33_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_35_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_36_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_37_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_38_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_39_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_3_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_40_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_41_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_42_p0_annotated.csv', 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Functions needed for annotation¶
In [324]:
## function to parse the events into chunks
# Function to get chunks from a column
def get_chunks(df, time_col, event_col):
chunks = []
current_chunk = None
for idx, row in df.iterrows():
if current_chunk is None:
current_chunk = {'value': row[event_col], 'start_idx': idx, 'start_time': row[time_col]}
elif row[event_col] != current_chunk['value']:
current_chunk['end_idx'] = idx - 1
current_chunk['end_time'] = df.loc[idx - 1, time_col]
chunks.append(current_chunk)
current_chunk = {'value': row[event_col], 'start_idx': idx, 'start_time': row[time_col]}
if idx == len(df) - 1:
current_chunk['end_idx'] = idx
current_chunk['end_time'] = row[time_col]
chunks.append(current_chunk)
chunk_data = []
for chunk in chunks:
duration = chunk['end_time'] - chunk['start_time']
chunk_data.append([chunk['value'], chunk['start_idx'], chunk['end_idx'], chunk['start_time'], chunk['end_time'], duration])
chunks_df = pd.DataFrame(chunk_data, columns=['value', 'start_idx', 'end_idx', 'start_time', 'end_time', 'duration'])
return chunks_df
## detect and merge short pauses
def detect_and_merge_short_pauses(chunk_df, col, df):
short_pauses = []
# Print rows that have value 0 and duration less than 250
if 'Wrist' in col or 'Elbow' in col:
pause_threshold = 250
elif 'Shoulder' in col:
pause_threshold = 450
elif 'Knee' in col:
pause_threshold = 300
# elif 'Head' in col:
# pause_threshold = 500
else:
pause_threshold = 350
short_pause_rows = chunk_df[(chunk_df['value'] == 0) & (chunk_df['duration'] < pause_threshold)]
# if short_pause_rows is not empty, loop over
if not short_pause_rows.empty:
# if these rows do not contain first and last row, append them to short_pauses
for idx, row in short_pause_rows.iterrows():
# if start_idx is not 0 and end_idx is not the last index, append to short_pauses
if row['start_idx'] != 0 and row['end_idx'] != len(df) - 1:
short_pauses.append(row)
# If short pauses is empty, we do not need to merge anything
if not short_pauses:
print('We do not need to merge')
df = df
return df
print('We need to merge')
# Check what is the value of the previous row and the next row in chunk
for pause in short_pauses:
# Get the index of the row in df
idx = pause.name
# Get the index of the previous row
prev_idx = idx - 1
# Get the start index of the previous row
new_start_index = chunk_df.loc[prev_idx, 'start_idx']
# Value of the previous row
prev_value = chunk_df.loc[prev_idx, 'value']
# Get the index of the next row
next_idx = idx + 1
# Get the end index of the next row
new_end_idx = chunk_df.loc[next_idx, 'end_idx']
# Everything from new_start_index to new_end_idx should be merged and named as previous value
# In the original dataframe, set the value to the previous value in this range
df.loc[new_start_index:new_end_idx, col] = prev_value
return df
## merge nonzero segments
def merge_nonzero_segments(chunks):
merged_all = []
for df in chunks:
# Filter rows with non-zero values
non_zero_rows = df[df['value'] != 0].reset_index(drop=True)
#print(non_zero_rows)
# Initialize list to store merged segments
merged_segments = []
# If the non_zero rows are empty, we do not need to do anything
if non_zero_rows.empty:
print('No non-zero rows')
continue
else:
# Initialize variables to track current segment
current_segment = non_zero_rows.iloc[0].copy()
# Iterate through non-zero rows to merge consecutive segments
for i in range(1, len(non_zero_rows)):
row = non_zero_rows.iloc[i]
if current_segment['end_idx'] + 1 == row['start_idx']:
# Merge segments
current_segment['end_idx'] = row['end_idx']
current_segment['duration'] += row['duration']
else:
# Append current segment to merged segments list
merged_segments.append(current_segment)
# Start new segment
current_segment = row.copy()
# Append the last segment
merged_segments.append(current_segment)
# Convert list of merged segments to DataFrame
merged_df = pd.DataFrame(merged_segments)
# Append to merged_all
merged_all.append(merged_df)
return merged_all
## process fake events
def process_fake_events(chunk, df_all):
#for chunk in chunks:
col = chunk['column'][0]
df = chunk
## FAKE EVENTS
nomov = []
if 'Wrist' in col:
fake_threshold = 400
if 'Elbow' in col:
fake_threshold = 350
elif 'Shoulder' in col:
fake_threshold = 350
elif 'Knee' in col:
fake_threshold = 300
elif 'Head' in col:
fake_threshold = 150
else:
fake_threshold = 400
# Print rows that don't have value 0 and duration less than x
fake_event_rows = df[(df['value'] != 0) & (df['duration'] < fake_threshold)]
print(fake_event_rows)
# Add those rows to nomov
for idx, row in fake_event_rows.iterrows():
nomov.append(row)
# If nomov is empty, we do not need to do anything
if not nomov:
print('No fake events found')
df_all = df_all
else:
# These fake events need to be turned into 0s in the original dataframe
print('We need to turn fake events into 0s')
# For each row in nomov, set the value in the original dataframe to 0
for fake_event in nomov:
start = int(fake_event['start_idx'])
end = int(fake_event['end_idx'])
# In the original dataframe, set the value to 0 in this range
df_all.loc[start:end, col] = 0
return df_all
# Function to check if there is any overlap
def check_overlap(df, non_zero_df, zero_df, wrist_name):
first_non_zero = non_zero_df.iloc[0]
last_non_zero = non_zero_df.iloc[-1]
startstodel = []
endstodel = []
for zero_row in zero_df.itertuples(index=False):
# Check if the first non-zero chunk is completely within the zero chunk
if first_non_zero.start_idx >= zero_row.start_idx and first_non_zero.end_idx <= zero_row.end_idx:
print(f"First non-zero chunk {first_non_zero.start_idx}-{first_non_zero.end_idx} in {first_non_zero.column} is completely within zero chunk {zero_row.start_idx}-{zero_row.end_idx} in {wrist_name}_vert_vel_movement_event")
# attach the start and end index of this chunk, but only if the nonzero chunk is smaller than 100
if (first_non_zero.end_idx - first_non_zero.start_idx) <= 50:
startstodel.append(first_non_zero.start_idx)
endstodel.append(first_non_zero.end_idx)
# check if there is partial overlap
elif first_non_zero.start_idx > zero_row.start_idx and first_non_zero.end_idx > zero_row.start_idx and first_non_zero.end_idx > zero_row.end_idx and first_non_zero.start_idx < zero_row.end_idx:
print(f"First non-zero chunk {first_non_zero.start_idx}-{first_non_zero.end_idx} in {first_non_zero.column} partially overlaps with zero chunk {zero_row.start_idx}-{zero_row.end_idx} in {wrist_name}_vert_vel_movement_event")
# attach the start and end index of this chunk
if (zero_row.end_idx - first_non_zero.start_idx) <= 50:
startstodel.append(first_non_zero.start_idx)
endstodel.append(zero_row.end_idx)
else:
print('no overlap')
# Check if the last non-zero chunk is completely within the zero chunk
if last_non_zero.start_idx >= zero_row.start_idx and last_non_zero.end_idx <= zero_row.end_idx:
print(f"Last non-zero chunk {last_non_zero.start_idx}-{last_non_zero.end_idx} in {last_non_zero.column} is completely within zero chunk {zero_row.start_idx}-{zero_row.end_idx} in {wrist_name}_vert_vel_movement_event")
# attach
if (last_non_zero.end_idx - last_non_zero.start_idx) <= 50:
# check if the last non-zero chunk is within the last 1/4 of idices
if last_non_zero.start_idx >= (len(df) - len(df)/4):
startstodel.append(last_non_zero.start_idx)
endstodel.append(last_non_zero.end_idx)
# check if there is partial overlap
elif last_non_zero.start_idx < zero_row.start_idx and last_non_zero.end_idx > zero_row.start_idx and last_non_zero.end_idx <= zero_row.end_idx:
print(f"Last non-zero chunk {last_non_zero.start_idx}-{last_non_zero.end_idx} in {last_non_zero.column} partially overlaps with zero chunk {zero_row.start_idx}-{zero_row.end_idx} in {wrist_name}_vert_vel_movement_event")
# attach
if (last_non_zero.end_idx - zero_row.start_idx) <= 50:
if last_non_zero.start_idx >= (len(df) - len(df)/4):
startstodel.append(zero_row.start_idx)
endstodel.append(last_non_zero.end_idx)
else:
print('no overlap')
return startstodel, endstodel
Loop over files and annotate events¶
In [325]:
sample = pd.read_csv(annofiles[0])
eventcols = [x for x in sample.columns if 'event' in x]
eventcols = [x for x in eventcols if 'Wrist' not in x]
for file in annofiles:
print('working on ' + file)
### file
file_df = pd.read_csv(file)
trialid = file_df['TrialID'][0]
##### BODY EXCEPT WRIST #####
#### merging short pauses
# Initialize variables
chunks = []
current_chunk = None
# Dictionary to store results for each column
chunk_results = {}
# Loop through each event column and apply the function
for col in eventcols:
chunks_df = get_chunks(file_df, 'Time', col)
chunk_results[col] = chunks_df
# turn the dictionary into a list, also with info about column
chunks = []
for col, df in chunk_results.items():
df['column'] = col
chunks.append(df)
# merge short pauses
for chunk in chunks:
col = chunk['column'][0]
df = chunk
file_df1 = detect_and_merge_short_pauses(df, col, file_df)
#### merging non-zero segments
# apply the chunk function again on the new dataframe
chunk_results_new = {}
# Loop through each event column and apply the function
for col in eventcols:
chunks_df = get_chunks(file_df1, 'Time', col)
chunk_results_new[col] = chunks_df
# turn the dictionary into a list, also with info about column
chunks_new = []
for col, df in chunk_results_new.items():
df['column'] = col
chunks_new.append(df)
# merge nonzero segments
merged_all = merge_nonzero_segments(chunks_new)
for chunk in merged_all:
for _, row in chunk.iterrows():
start = row['start_idx']
end = row['end_idx']
value = row['value']
column = row['column']
# Update the corresponding column in file_df
file_df1.loc[start:end, column] = value
### process fake events
# perform the chunk function again
chunk_results_final = {}
for col in eventcols:
chunks_df = get_chunks(file_df1, 'Time', col)
chunk_results_final[col] = chunks_df
# convert dict into list
chunks_final = []
for col, df in chunk_results_final.items():
df['column'] = col
chunks_final.append(df)
# get rid of fake events
for chunk in chunks_final:
# process fake events
file_df2 = process_fake_events(chunk, file_df1)
#### WRIST ####
# lets do chunks again, but only on wrist event cols
wristcols = [x for x in file_df2.columns if 'Wrist' in x]
wristcols = [x for x in wristcols if 'event' in x]
#### first only vert_vel
vvcols = [x for x in wristcols if 'vert_vel' in x]
### merging short pauses
# chunks
vvchunks_results = {}
for col in vvcols:
vvchunks_df = get_chunks(file_df2, 'Time', col)
vvchunks_results[col] = vvchunks_df
# convert dict into list
vvchunks = []
for col, df in vvchunks_results.items():
df['column'] = col
vvchunks.append(df)
# merge short pauses
for chunk in vvchunks:
col = chunk['column'][0]
df = chunk
file_df3 = detect_and_merge_short_pauses(df, col, file_df2)
### merging non-zero segments
# apply the chunk function again on the new dataframe
vvchunk_results_new = {}
for col in vvcols:
vvchunks_df = get_chunks(file_df3, 'Time', col)
vvchunk_results_new[col] = vvchunks_df
# convert dict into list
vvchunks_new = []
for col, df in vvchunk_results_new.items():
df['column'] = col
vvchunks_new.append(df)
# merge events
vvmerged_all = merge_nonzero_segments(vvchunks_new)
for chunk in vvmerged_all:
for _, row in chunk.iterrows():
start = row['start_idx']
end = row['end_idx']
value = row['value']
column = row['column']
# Update the corresponding column in file_df
file_df3.loc[start:end, column] = value
### process fake events
# perform the chunk function again
vvchunk_results_final = {}
for col in vvcols:
vvchunks_df = get_chunks(file_df3, 'Time', col)
vvchunk_results_final[col] = vvchunks_df
# convert dict into list
vvchunks_final = []
for col, df in vvchunk_results_final.items():
df['column'] = col
vvchunks_final.append(df)
for chunk in vvchunks_final:
# delete fake events
file_df4 = process_fake_events(chunk, file_df3)
# now we have to check for overlap
# now the chunk function
wchunk_results = {}
for col in wristcols:
wchunks_df = get_chunks(file_df4, 'Time', col)
wchunk_results[col] = wchunks_df
# convert dict into list
wchunks = []
for col, df in wchunk_results.items():
df['column'] = col
wchunks.append(df)
# find the chunk in wchunks_results_final_list tHAT HAS column value RWrist_speed_event
rightspeed = next((df for df in wchunks if 'RWrist_speed_event' in df['column'].values), None)
leftspeed = next((df for df in wchunks if 'LWrist_speed_event' in df['column'].values), None)
rightvel = next((df for df in wchunks if 'RWrist_vert_vel_movement_event' in df['column'].values), None)
leftvel = next((df for df in wchunks if 'LWrist_vert_vel_movement_event' in df['column'].values), None)
lefttocheck = leftspeed[leftspeed['value'] != 0]
righttocheck = rightspeed[rightspeed['value'] != 0]
leftzero = leftvel[leftvel['value'] == 0]
rightzero = rightvel[rightvel['value'] == 0]
# if lefttocheck is not empty, check for overlaps
if not lefttocheck.empty:
print("Checking LWrist_event vs LWrist_vv_event")
sindex1, eindex1 = check_overlap(file_df4, lefttocheck, leftzero, "LWrist")
# replace the values in the original dataframe with 0
for start, end in zip(sindex1, eindex1):
file_df4.loc[start:end, 'LWrist_speed_event'] = 0
# if righttocheck is not empty, check for overlaps
if not righttocheck.empty:
print("\nChecking RWrist_event vs RWrist_vv_event")
sindex2, eindex2 =check_overlap(file_df4, righttocheck, rightzero, "RWrist")
# replace the values in the original dataframe with 0
for start, end in zip(sindex2, eindex2):
# within this indices, replace the values with 0
file_df4.loc[start:end, 'RWrist_speed_event'] = 0
### merging short pauses
# now we work with speed only
wristcols = [x for x in wristcols if 'speed' in x]
# merge short pauses
# get chunks
wchunk_results_new = {}
for col in wristcols:
wchunks_df = get_chunks(file_df4, 'Time', col)
wchunk_results_new[col] = wchunks_df
# convert dict into list
wchunks_new = []
for col, df in wchunk_results_new.items():
df['column'] = col
wchunks_new.append(df)
for chunk in wchunks_new:
col = chunk['column'][0]
df = chunk
# detect and merge short pauses
file_df5 = detect_and_merge_short_pauses(df, col, file_df4)
### merging non-zero segments
# apply the chunk function again on the new dataframe
wchunk_results_new2 = {}
for col in wristcols:
wchunks_df = get_chunks(file_df5, 'Time', col)
wchunk_results_new2[col] = wchunks_df
# convert dict into list
wchunks_new2 = []
for col, df in wchunk_results_new2.items():
df['column'] = col
wchunks_new2.append(df)
# merge events
wmerged_all = merge_nonzero_segments(wchunks_new2)
for chunk in wmerged_all:
for _, row in chunk.iterrows():
#print(row)
start = row['start_idx']
end = row['end_idx']
value = row['value']
column = row['column']
# Update the corresponding column in file_df
file_df5.loc[start:end, column] = value
### process fake events
# perform the chunk function again
wchunk_results_final = {}
for col in wristcols:
wchunks_df = get_chunks(file_df5, 'Time', col)
wchunk_results_final[col] = wchunks_df
# convert dict into list
wchunks_final = []
for col, df in wchunk_results_final.items():
df['column'] = col
wchunks_final.append(df)
# get rid of fake events
for chunk in wchunks_final:
file_df6 = process_fake_events(chunk, file_df5)
### SAVING
# make copy of file_df that has only 'event' columns in
df_final = file_df6.copy()
# drop other columns
df_final = df_final[[x for x in df_final.columns if 'event' in x or 'Time' in x or 'TrialID' in x]]
#for each column, everywhere where is 0, put 'nomovement', elsewhere 'movement'
for col in df_final.columns:
if 'event' in col:
df_final[col] = np.where(df_final[col] == 0, 'nomovement', 'movement')
# save the annotated file
df_final.to_csv(annofolder + '/' + trialid + '_ELAN_anno.csv', index=False)
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_0_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 102 106 1700.0 1766.666667 66.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 14 0.000000 233.333333 233.333333
2 2 93 103 1550.000000 1716.666667 166.666667
4 3 224 235 3733.333333 3916.666667 183.333333
column
0 RElbow_speed_event
2 RElbow_speed_event
4 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-15 in LWrist_speed_event is completely within zero chunk 0-299 in LWrist_vert_vel_movement_event
Last non-zero chunk 221-237 in LWrist_speed_event is completely within zero chunk 0-299 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-14 in RWrist_speed_event is completely within zero chunk 0-299 in RWrist_vert_vel_movement_event
Last non-zero chunk 218-238 in RWrist_speed_event is completely within zero chunk 0-299 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
3 3 218 238 3633.333333 3966.666667 333.333333
column
3 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 221 237 3683.333333 3950.0 266.666667
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_10_p1_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 287 301 4783.333333 5016.666667 233.333333
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 141 144 2350.000000 2400.000000 50.0
3 2 292 298 4866.666667 4966.666667 100.0
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 180 189 3000.0 3150.000000 150.000000
3 2 294 298 4900.0 4966.666667 66.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 285 299 4750.0 4983.333333 233.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 284-306 in LWrist_speed_event is completely within zero chunk 0-318 in LWrist_vert_vel_movement_event
Last non-zero chunk 284-306 in LWrist_speed_event is completely within zero chunk 0-318 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 283-309 in RWrist_speed_event is completely within zero chunk 0-318 in RWrist_vert_vel_movement_event
Last non-zero chunk 283-309 in RWrist_speed_event is completely within zero chunk 0-318 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_11_p1_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 163 179 2716.666667 2983.333333 266.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 163 176 2716.666667 2933.333333 216.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 158-185 in LWrist_speed_event is completely within zero chunk 0-187 in LWrist_vert_vel_movement_event
Last non-zero chunk 158-185 in LWrist_speed_event is completely within zero chunk 0-187 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 159-187 in RWrist_speed_event is completely within zero chunk 0-187 in RWrist_vert_vel_movement_event
Last non-zero chunk 159-187 in RWrist_speed_event is completely within zero chunk 0-187 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_12_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 211 218 3516.666667 3633.333333 116.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 213 218 3550.0 3633.333333 83.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 210 218 3500.0 3633.333333 133.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 203-218 in LWrist_speed_event is completely within zero chunk 0-218 in LWrist_vert_vel_movement_event
Last non-zero chunk 203-218 in LWrist_speed_event is completely within zero chunk 0-218 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 204-218 in RWrist_speed_event is completely within zero chunk 0-218 in RWrist_vert_vel_movement_event
Last non-zero chunk 204-218 in RWrist_speed_event is completely within zero chunk 0-218 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_13_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 195 202 3250.0 3366.666667 116.666667
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 187-202 in LWrist_speed_event is completely within zero chunk 0-202 in LWrist_vert_vel_movement_event
Last non-zero chunk 187-202 in LWrist_speed_event is completely within zero chunk 0-202 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 187-202 in RWrist_speed_event is completely within zero chunk 0-202 in RWrist_vert_vel_movement_event
Last non-zero chunk 187-202 in RWrist_speed_event is completely within zero chunk 0-202 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_14_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 77 88 1283.333333 1466.666667 183.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 79 87 1316.666667 1450.0 133.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 81 92 1350.0 1533.333333 183.333333
3 2 276 276 4600.0 4600.000000 0.000000
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 273 276 4550.0 4600.0 50.0
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 67-92 in LWrist_speed_event is completely within zero chunk 0-276 in LWrist_vert_vel_movement_event
Last non-zero chunk 267-276 in LWrist_speed_event is completely within zero chunk 0-276 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 68-97 in RWrist_speed_event is completely within zero chunk 0-276 in RWrist_vert_vel_movement_event
Last non-zero chunk 258-276 in RWrist_speed_event is completely within zero chunk 0-276 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_15_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 80 85 1333.333333 1416.666667 83.333333
column
1 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 238 239 3966.666667 3983.333333 16.666667
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 224-239 in LWrist_speed_event is completely within zero chunk 0-239 in LWrist_vert_vel_movement_event
Last non-zero chunk 224-239 in LWrist_speed_event is completely within zero chunk 0-239 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 226-239 in RWrist_speed_event is completely within zero chunk 0-239 in RWrist_vert_vel_movement_event
Last non-zero chunk 226-239 in RWrist_speed_event is completely within zero chunk 0-239 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_16_p1_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 164 165 2733.333333 2750.0 16.666667
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 54 64 900.000000 1066.666667 166.666667
3 2 185 198 3083.333333 3300.000000 216.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 55 63 916.666667 1050.000000 133.333333
3 2 188 197 3133.333333 3283.333333 150.000000
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 58 71 966.666667 1183.333333 216.666667
3 2 190 205 3166.666667 3416.666667 250.000000
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 48-72 in LWrist_speed_event is completely within zero chunk 0-211 in LWrist_vert_vel_movement_event
Last non-zero chunk 187-211 in LWrist_speed_event is completely within zero chunk 0-211 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 47-79 in RWrist_speed_event is completely within zero chunk 0-211 in RWrist_vert_vel_movement_event
Last non-zero chunk 185-211 in RWrist_speed_event is completely within zero chunk 0-211 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_17_p1_annotated.csv
We need to merge
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 208 226 3466.666667 3766.666667 300.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 63 73 1050.000000 1216.666667 166.666667
3 2 224 242 3733.333333 4033.333333 300.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 230 238 3833.333333 3966.666667 133.333333
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 47 60 783.333333 1000.000000 216.666667
3 2 232 239 3866.666667 3983.333333 116.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 47 57 783.333333 950.0 166.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 51 67 850.0 1116.666667 266.666667
3 2 228 244 3800.0 4066.666667 266.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 41-72 in LWrist_speed_event is completely within zero chunk 0-255 in LWrist_vert_vel_movement_event
Last non-zero chunk 220-248 in LWrist_speed_event is completely within zero chunk 0-255 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 40-73 in RWrist_speed_event is completely within zero chunk 0-255 in RWrist_vert_vel_movement_event
Last non-zero chunk 223-255 in RWrist_speed_event is completely within zero chunk 0-255 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_18_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We do not need to merge
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 12 383 389 6383.333333 6483.333333 100.0
column
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 98 119 1633.333333 1983.333333 350.0
column
1 RHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 155 158 2583.333333 2633.333333 50.0
column
3 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 297 306 4950.000000 5100.000000 150.000000
5 3 334 344 5566.666667 5733.333333 166.666667
7 4 365 377 6083.333333 6283.333333 200.000000
column
3 RElbow_speed_event
5 RElbow_speed_event
7 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 167 168 2783.333333 2800.000000 16.666667
7 7 273 296 4550.000000 4933.333333 383.333333
9 9 343 351 5716.666667 5850.000000 133.333333
column
3 LHip_speed_event
7 LHip_speed_event
9 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 79-143 in LWrist_speed_event partially overlaps with zero chunk 0-87 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 353-375 in RWrist_speed_event is completely within zero chunk 270-389 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_19_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 80 83 1333.333333 1383.333333 50.0
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 67 70 1116.666667 1166.666667 50.000000
5 4 247 257 4116.666667 4283.333333 166.666667
7 5 339 352 5650.000000 5866.666667 216.666667
column
1 RShoulder_speed_event
5 RShoulder_speed_event
7 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 127 144 2116.666667 2400.000000 283.333333
5 3 221 229 3683.333333 3816.666667 133.333333
9 5 330 343 5500.000000 5716.666667 216.666667
column
3 RElbow_speed_event
5 RElbow_speed_event
9 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 98 108 1633.333333 1800.0 166.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 331-344 in LWrist_speed_event is completely within zero chunk 317-352 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 23-83 in RWrist_speed_event partially overlaps with zero chunk 0-25 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 310-339 in RWrist_speed_event partially overlaps with zero chunk 337-352 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_1_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_20_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 149 156 2483.333333 2600.0 116.666667
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 63 80 1050.0 1333.333333 283.333333
3 2 180 193 3000.0 3216.666667 216.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We do not need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 23-72 in LWrist_speed_event partially overlaps with zero chunk 0-38 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 27-77 in RWrist_speed_event partially overlaps with zero chunk 0-40 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 183-232 in RWrist_speed_event partially overlaps with zero chunk 226-232 in RWrist_vert_vel_movement_event
We do not need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_21_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 162 181 2700.0 3016.666667 316.666667
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 32-62 in LWrist_speed_event partially overlaps with zero chunk 0-34 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 37-69 in RWrist_speed_event partially overlaps with zero chunk 0-38 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 162-200 in RWrist_speed_event partially overlaps with zero chunk 198-215 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_22_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
3 2 272 274 4533.333333 4566.666667 33.333333
column
3 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 58 60 966.666667 1000.0 33.333333
3 2 224 225 3733.333333 3750.0 16.666667
column
1 Head_speed_event
3 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 191 206 3183.333333 3433.333333 250.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 187 193 3116.666667 3216.666667 100.000000
5 3 249 254 4150.000000 4233.333333 83.333333
column
3 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 207 215 3450.0 3583.333333 133.333333
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 186 206 3100.0 3433.333333 333.333333
3 2 255 261 4250.0 4350.000000 100.000000
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 35-101 in LWrist_speed_event partially overlaps with zero chunk 0-38 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 248-261 in LWrist_speed_event is completely within zero chunk 104-274 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-102 in RWrist_speed_event partially overlaps with zero chunk 0-38 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 240-262 in RWrist_speed_event is completely within zero chunk 105-274 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_23_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 174 190 2900.0 3166.666667 266.666667
column
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 31-54 in LWrist_speed_event is completely within zero chunk 0-231 in LWrist_vert_vel_movement_event
Last non-zero chunk 203-231 in LWrist_speed_event is completely within zero chunk 0-231 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 34-86 in RWrist_speed_event partially overlaps with zero chunk 0-55 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 173-208 in RWrist_speed_event partially overlaps with zero chunk 207-231 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_24_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 87 100 1450.000000 1666.666667 216.666667
3 2 121 137 2016.666667 2283.333333 266.666667
7 5 257 270 4283.333333 4500.000000 216.666667
column
1 LElbow_speed_event
3 LElbow_speed_event
7 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 86 99 1433.333333 1650.0 216.666667
5 5 291 294 4850.000000 4900.0 50.000000
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 50 61 833.333333 1016.666667 183.333333
3 2 281 295 4683.333333 4916.666667 233.333333
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration column
1 1 222 225 3700.0 3750.0 50.0 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 154 156 2566.666667 2600.000000 33.333333
5 4 219 222 3650.000000 3700.000000 50.000000
7 5 287 293 4783.333333 4883.333333 100.000000
column
3 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
3 9 288 299 4800.0 4983.333333 183.333333
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 62-248 in LWrist_speed_event partially overlaps with zero chunk 0-68 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 308-316 in LWrist_speed_event is completely within zero chunk 294-342 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 42-273 in RWrist_speed_event partially overlaps with zero chunk 0-64 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 279-313 in RWrist_speed_event is completely within zero chunk 270-342 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_25_p0_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 4 313 334 5216.666667 5566.666667 350.0
column
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 181 182 3016.666667 3033.333333 16.666667
5 4 246 263 4100.000000 4383.333333 283.333333
column
3 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 308 323 5133.333333 5383.333333 250.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
value start_idx end_idx start_time end_time duration \
3 2 232 255 3866.666667 4250.0 383.333333
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 240 255 4000.0 4250.0 250.0
column
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 22-74 in LWrist_speed_event partially overlaps with zero chunk 0-41 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 27-76 in RWrist_speed_event partially overlaps with zero chunk 0-46 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_26_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 162 172 2700.0 2866.666667 166.666667
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 31 35 516.666667 583.333333 66.666667
5 3 171 185 2850.000000 3083.333333 233.333333
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 123 131 2050.0 2183.333333 133.333333
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 73 85 1216.666667 1416.666667 200.000000
3 2 203 208 3383.333333 3466.666667 83.333333
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 33-47 in LWrist_speed_event is completely within zero chunk 0-67 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 225-230 in LWrist_speed_event is completely within zero chunk 206-249 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 30-49 in RWrist_speed_event is completely within zero chunk 0-56 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 221-230 in RWrist_speed_event is completely within zero chunk 194-249 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 189 202 3150.0 3366.666667 216.666667
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_27_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 191 198 3183.333333 3300.0 116.666667
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 113 114 1883.333333 1900.000000 16.666667
3 2 156 158 2600.000000 2633.333333 33.333333
5 3 278 279 4633.333333 4650.000000 16.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 46 55 766.666667 916.666667 150.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 35 47 583.333333 783.333333 200.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 4 255 273 4250.0 4550.000000 300.000000
5 5 300 316 5000.0 5266.666667 266.666667
column
3 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 166 184 2766.666667 3066.666667 300.000000
3 3 299 300 4983.333333 5000.000000 16.666667
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 35 48 583.333333 800.0 216.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 34 52 566.666667 866.666667 300.0
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 31 44 516.666667 733.333333 216.666667
5 4 176 198 2933.333333 3300.000000 366.666667
column
1 LWrist_vert_vel_movement_event
5 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 27-58 in LWrist_speed_event is completely within zero chunk 0-125 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 96-312 in LWrist_speed_event partially overlaps with zero chunk 267-361 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 30-63 in RWrist_speed_event is completely within zero chunk 0-275 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 104-320 in RWrist_speed_event partially overlaps with zero chunk 318-361 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_28_p1_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 4 0.0 66.666667 66.666667
column
0 LHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 3 204 212 3400.0 3533.333333 133.333333
column
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 82 86 1366.666667 1433.333333 66.666667
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 202 214 3366.666667 3566.666667 200.0
column
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 55 61 916.666667 1016.666667 100.000000
3 2 207 214 3450.000000 3566.666667 116.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 41-60 in LWrist_speed_event is completely within zero chunk 0-67 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 157-217 in LWrist_speed_event partially overlaps with zero chunk 192-279 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 43-105 in RWrist_speed_event partially overlaps with zero chunk 0-48 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 155-219 in RWrist_speed_event partially overlaps with zero chunk 217-279 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_29_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 42 45 700.0 750.0 50.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 142 142 2366.666667 2366.666667 0.0
5 3 188 200 3133.333333 3333.333333 200.0
column
3 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 179 192 2983.333333 3200.0 216.666667
column
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 52 53 866.666667 883.333333 16.666667
3 2 71 89 1183.333333 1483.333333 300.000000
5 4 117 138 1950.000000 2300.000000 350.000000
7 6 180 191 3000.000000 3183.333333 183.333333
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
5 RWrist_vert_vel_movement_event
7 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 44 58 733.333333 966.666667 233.333333
3 2 94 116 1566.666667 1933.333333 366.666667
7 6 193 217 3216.666667 3616.666667 400.000000
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
7 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-216 in LWrist_speed_event partially overlaps with zero chunk 0-140 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 253-261 in LWrist_speed_event is completely within zero chunk 169-261 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-194 in RWrist_speed_event is completely within zero chunk 0-261 in RWrist_vert_vel_movement_event
Last non-zero chunk 255-261 in RWrist_speed_event is completely within zero chunk 0-261 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_2_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 118 119 1966.666667 1983.333333 16.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 121 126 2016.666667 2100.0 83.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 117 126 1950.0 2100.0 150.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 119-144 in LWrist_speed_event is completely within zero chunk 0-165 in LWrist_vert_vel_movement_event
Last non-zero chunk 119-144 in LWrist_speed_event is completely within zero chunk 0-165 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 117-146 in RWrist_speed_event is completely within zero chunk 0-165 in RWrist_vert_vel_movement_event
Last non-zero chunk 117-146 in RWrist_speed_event is completely within zero chunk 0-165 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_30_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 51 53 850.0 883.333333 33.333333
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 61 71 1016.666667 1183.333333 166.666667
3 2 175 187 2916.666667 3116.666667 200.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 46 65 766.666667 1083.333333 316.666667
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 46 56 766.666667 933.333333 166.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 37-71 in LWrist_speed_event is completely within zero chunk 0-226 in LWrist_vert_vel_movement_event
Last non-zero chunk 170-190 in LWrist_speed_event is completely within zero chunk 0-226 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 150-193 in RWrist_speed_event partially overlaps with zero chunk 186-226 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 170 190 2833.333333 3166.666667 333.333333
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_31_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 171 187 2850.0 3116.666667 266.666667
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 50 51 833.333333 850.0 16.666667
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
Last non-zero chunk 156-187 in LWrist_speed_event is completely within zero chunk 120-230 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 167-182 in RWrist_speed_event is completely within zero chunk 0-230 in RWrist_vert_vel_movement_event
Last non-zero chunk 167-182 in RWrist_speed_event is completely within zero chunk 0-230 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_32_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 223 223 3716.666667 3716.666667 0.0
column
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 232 248 3866.666667 4133.333333 266.666667
column
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 152 152 2533.333333 2533.333333 0.0
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 114 134 1900.0 2233.333333 333.333333
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 106-271 in LWrist_speed_event partially overlaps with zero chunk 0-165 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 106-271 in LWrist_speed_event partially overlaps with zero chunk 244-299 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 106-267 in RWrist_speed_event partially overlaps with zero chunk 0-107 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_33_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 9 9 150.000000 150.000000 0.000000
5 4 181 193 3016.666667 3216.666667 200.000000
7 6 219 219 3650.000000 3650.000000 0.000000
9 7 284 289 4733.333333 4816.666667 83.333333
column
1 RHeel_speed_event
5 RHeel_speed_event
7 RHeel_speed_event
9 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 42 51 700.000000 850.000000 150.000000
3 2 92 98 1533.333333 1633.333333 100.000000
9 5 281 283 4683.333333 4716.666667 33.333333
column
1 LElbow_speed_event
3 LElbow_speed_event
9 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 72 74 1200.0 1233.333333 33.333333
3 2 120 122 2000.0 2033.333333 33.333333
column
1 Head_speed_event
3 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 109 124 1816.666667 2066.666667 250.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 42 57 700.0 950.0 250.0
5 4 270 291 4500.0 4850.0 350.0
column
1 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 28 51 466.666667 850.0 383.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 34-62 in LWrist_speed_event partially overlaps with zero chunk 0-35 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 30-54 in RWrist_speed_event is completely within zero chunk 0-276 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
value start_idx end_idx start_time end_time duration \
1 2 230 241 3833.333333 4016.666667 183.333333
column
1 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_35_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 50 55 833.333333 916.666667 83.333333
3 2 82 88 1366.666667 1466.666667 100.000000
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 37-87 in LWrist_speed_event partially overlaps with zero chunk 0-62 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 236-272 in LWrist_speed_event partially overlaps with zero chunk 265-310 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 43-98 in RWrist_speed_event is completely within zero chunk 0-119 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 236-272 in RWrist_speed_event is completely within zero chunk 168-310 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_36_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 32 43 533.333333 716.666667 183.333333
3 2 60 72 1000.000000 1200.000000 200.000000
5 3 254 264 4233.333333 4400.000000 166.666667
column
1 LElbow_speed_event
3 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 61 77 1016.666667 1283.333333 266.666667
3 2 216 219 3600.000000 3650.000000 50.000000
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 237 256 3950.0 4266.666667 316.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 229 250 3816.666667 4166.666667 350.0
column
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 25-42 in LWrist_speed_event is completely within zero chunk 0-55 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 231-264 in LWrist_speed_event is completely within zero chunk 87-295 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 28-44 in RWrist_speed_event is completely within zero chunk 0-51 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 224-256 in RWrist_speed_event partially overlaps with zero chunk 253-295 in RWrist_vert_vel_movement_event
We need to merge
value start_idx end_idx start_time end_time duration \
3 4 197 199 3283.333333 3316.666667 33.333333
column
3 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_37_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 123 146 2050.0 2433.333333 383.333333
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 178 184 2966.666667 3066.666667 100.0
column
3 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-53 in LWrist_speed_event is completely within zero chunk 0-164 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-68 in RWrist_speed_event partially overlaps with zero chunk 0-40 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 111 115 1850.0 1916.666667 66.666667
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_38_p0_annotated.csv
We need to merge
We need to merge
We do not need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 70 78 1166.666667 1300.0 133.333333
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 15 23 250.000000 383.333333 133.333333
3 2 65 81 1083.333333 1350.000000 266.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 25 34 416.666667 566.666667 150.0
5 3 125 131 2083.333333 2183.333333 100.0
column
1 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 18 23 300.000000 383.333333 83.333333
3 2 65 84 1083.333333 1400.000000 316.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 14-42 in LWrist_speed_event is completely within zero chunk 0-322 in LWrist_vert_vel_movement_event
Last non-zero chunk 266-292 in LWrist_speed_event is completely within zero chunk 0-322 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 15-35 in RWrist_speed_event is completely within zero chunk 0-46 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 146-286 in RWrist_speed_event partially overlaps with zero chunk 283-322 in RWrist_vert_vel_movement_event
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_39_p0_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 80 87 1333.333333 1450.0 116.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 30 38 500.000000 633.333333 133.333333
5 4 186 195 3100.000000 3250.000000 150.000000
7 5 265 271 4416.666667 4516.666667 100.000000
column
1 RElbow_speed_event
5 RElbow_speed_event
7 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 99 115 1650.0 1916.666667 266.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 3 187 208 3116.666667 3466.666667 350.0
column
5 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 23-46 in LWrist_speed_event is completely within zero chunk 0-78 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 23-42 in RWrist_speed_event is completely within zero chunk 0-79 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
value start_idx end_idx start_time end_time duration \
5 4 188 206 3133.333333 3433.333333 300.000000
7 5 282 283 4700.000000 4716.666667 16.666667
column
5 RWrist_speed_event
7 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 4 189 212 3150.0 3533.333333 383.333333
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_3_p0_annotated.csv
We do not need to merge
We do not need to merge
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 6 16 100.0 266.666667 166.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 16 0.0 266.666667 266.666667
2 2 510 523 8500.0 8716.666667 216.666667
column
0 RShoulder_speed_event
2 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 19 0.000000 316.666667 316.666667
2 2 511 519 8516.666667 8650.000000 133.333333
column
0 RElbow_speed_event
2 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 11 12 183.333333 200.0 16.666667
column
1 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 4 0.0 66.666667 66.666667
column
0 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 15 0.000000 250.0 250.000000
2 2 520 522 8666.666667 8700.0 33.333333
column
0 LShoulder_speed_event
2 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 5-30 in LWrist_speed_event is completely within zero chunk 0-533 in LWrist_vert_vel_movement_event
Last non-zero chunk 504-517 in LWrist_speed_event is completely within zero chunk 0-533 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-31 in RWrist_speed_event is completely within zero chunk 0-533 in RWrist_vert_vel_movement_event
Last non-zero chunk 501-532 in RWrist_speed_event is completely within zero chunk 0-533 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_40_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 46 51 766.666667 850.000000 83.333333
3 2 95 119 1583.333333 1983.333333 400.000000
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 20-70 in LWrist_speed_event is completely within zero chunk 0-83 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 82-136 in LWrist_speed_event partially overlaps with zero chunk 114-182 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 20-58 in RWrist_speed_event partially overlaps with zero chunk 0-30 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 121-135 in RWrist_speed_event is completely within zero chunk 101-182 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_41_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 323 324 5383.333333 5400.0 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 28 40 466.666667 666.666667 200.0
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 156 160 2600.0 2666.666667 66.666667
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 149 166 2483.333333 2766.666667 283.333333
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 24-43 in LWrist_speed_event is completely within zero chunk 0-364 in LWrist_vert_vel_movement_event
Last non-zero chunk 307-316 in LWrist_speed_event is completely within zero chunk 0-364 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 26-41 in RWrist_speed_event is completely within zero chunk 0-364 in RWrist_vert_vel_movement_event
Last non-zero chunk 311-328 in RWrist_speed_event is completely within zero chunk 0-364 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 151 167 2516.666667 2783.333333 266.666667
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_42_p0_annotated.csv
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 33 46 550.0 766.666667 216.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 167 181 2783.333333 3016.666667 233.333333
column
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
5 3 213 213 3550.0 3550.0 0.0
column
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 7-21 in LWrist_speed_event is completely within zero chunk 0-265 in LWrist_vert_vel_movement_event
Last non-zero chunk 208-228 in LWrist_speed_event is completely within zero chunk 0-265 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 4-64 in RWrist_speed_event partially overlaps with zero chunk 0-22 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 209-226 in RWrist_speed_event is completely within zero chunk 197-265 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_43_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 208 220 3466.666667 3666.666667 200.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 209 216 3483.333333 3600.0 116.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 15-68 in LWrist_speed_event partially overlaps with zero chunk 0-28 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 199-221 in LWrist_speed_event is completely within zero chunk 188-253 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 16-67 in RWrist_speed_event partially overlaps with zero chunk 0-28 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 144-223 in RWrist_speed_event partially overlaps with zero chunk 187-253 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_44_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
3 2 187 203 3116.666667 3383.333333 266.666667
column
3 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 132 148 2200.0 2466.666667 266.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 4 332 333 5533.333333 5550.0 16.666667
column
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 39 52 650.000000 866.666667 216.666667
3 2 72 85 1200.000000 1416.666667 216.666667
5 3 231 233 3850.000000 3883.333333 33.333333
7 4 259 269 4316.666667 4483.333333 166.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
5 RElbow_speed_event
7 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 225 225 3750.000000 3750.000000 0.000000
3 2 254 256 4233.333333 4266.666667 33.333333
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 5 333 333 5550.0 5550.0 0.0
column
5 LShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 36-49 in LWrist_speed_event is completely within zero chunk 0-80 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 224-277 in LWrist_speed_event partially overlaps with zero chunk 261-333 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 35-89 in RWrist_speed_event partially overlaps with zero chunk 0-56 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 329-333 in RWrist_speed_event is completely within zero chunk 264-333 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 150 162 2500.0 2700.0 200.0
5 4 186 189 3100.0 3150.0 50.0
column
3 LWrist_speed_event
5 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_45_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 48 50 800.000000 833.333333 33.333333
3 2 97 99 1616.666667 1650.000000 33.333333
5 3 162 174 2700.000000 2900.000000 200.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 50 62 833.333333 1033.333333 200.0
3 2 110 128 1833.333333 2133.333333 300.0
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 50 69 833.333333 1150.0 316.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 40 59 666.666667 983.333333 316.666667
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 193 213 3216.666667 3550.0 333.333333
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 35 44 583.333333 733.333333 150.000000
3 2 203 210 3383.333333 3500.000000 116.666667
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 39-45 in LWrist_speed_event is completely within zero chunk 0-261 in LWrist_vert_vel_movement_event
Last non-zero chunk 152-233 in LWrist_speed_event is completely within zero chunk 0-261 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
Last non-zero chunk 193-234 in RWrist_speed_event is completely within zero chunk 66-261 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_46_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 154 171 2566.666667 2850.0 283.333333
column
1 LHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 49 55 816.666667 916.666667 100.000000
3 2 84 86 1400.000000 1433.333333 33.333333
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 107 108 1783.333333 1800.000000 16.666667
3 2 144 145 2400.000000 2416.666667 16.666667
5 3 174 181 2900.000000 3016.666667 116.666667
column
1 LHip_speed_event
3 LHip_speed_event
5 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 58 70 966.666667 1166.666667 200.000000
3 2 175 177 2916.666667 2950.000000 33.333333
5 3 208 228 3466.666667 3800.000000 333.333333
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
5 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 222 229 3700.0 3816.666667 116.666667
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 51-63 in LWrist_speed_event is completely within zero chunk 0-339 in LWrist_vert_vel_movement_event
Last non-zero chunk 184-221 in LWrist_speed_event is completely within zero chunk 0-339 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 55-225 in RWrist_speed_event is completely within zero chunk 0-339 in RWrist_vert_vel_movement_event
Last non-zero chunk 55-225 in RWrist_speed_event is completely within zero chunk 0-339 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 115 132 1916.666667 2200.0 283.333333
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_47_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 101 125 1683.333333 2083.333333 400.000000
5 7 335 337 5583.333333 5616.666667 33.333333
column
1 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 87 101 1450.0 1683.333333 233.333333
3 2 123 127 2050.0 2116.666667 66.666667
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 94 98 1566.666667 1633.333333 66.666667
3 2 171 175 2850.000000 2916.666667 66.666667
column
1 Head_speed_event
3 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 244 250 4066.666667 4166.666667 100.0
column
3 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 87 98 1450.000000 1633.333333 183.333333
3 2 239 251 3983.333333 4183.333333 200.000000
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 79-105 in LWrist_speed_event is completely within zero chunk 0-350 in LWrist_vert_vel_movement_event
Last non-zero chunk 243-247 in LWrist_speed_event is completely within zero chunk 0-350 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 79-222 in RWrist_speed_event is completely within zero chunk 0-350 in RWrist_vert_vel_movement_event
Last non-zero chunk 230-252 in RWrist_speed_event is completely within zero chunk 0-350 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 202 206 3366.666667 3433.333333 66.666667
3 3 243 247 4050.000000 4116.666667 66.666667
column
1 LWrist_speed_event
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_48_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 137 159 2283.333333 2650.0 366.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 3 276 282 4600.000000 4700.000000 100.000000
7 4 314 322 5233.333333 5366.666667 133.333333
column
5 RElbow_speed_event
7 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 306 325 5100.0 5416.666667 316.666667
column
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 56 80 933.333333 1333.333333 400.000000
5 4 203 223 3383.333333 3716.666667 333.333333
column
1 RWrist_vert_vel_movement_event
5 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 313 321 5216.666667 5350.0 133.333333
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 231-247 in LWrist_speed_event is completely within zero chunk 0-353 in LWrist_vert_vel_movement_event
Last non-zero chunk 231-247 in LWrist_speed_event is completely within zero chunk 0-353 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 58-81 in RWrist_speed_event is completely within zero chunk 0-97 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
3 4 206 223 3433.333333 3716.666667 283.333333
column
3 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_49_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 83 83 1383.333333 1383.333333 0.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 64 68 1066.666667 1133.333333 66.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 61 72 1016.666667 1200.0 183.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 65-81 in LWrist_speed_event is completely within zero chunk 0-225 in LWrist_vert_vel_movement_event
Last non-zero chunk 157-177 in LWrist_speed_event is completely within zero chunk 0-225 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 157 177 2616.666667 2950.0 333.333333
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_4_p0_annotated.csv
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 274 276 4566.666667 4600.0 33.333333
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 252 263 4200.0 4383.333333 183.333333
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 261 264 4350.0 4400.0 50.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 60 67 1000.000000 1116.666667 116.666667
3 2 248 260 4133.333333 4333.333333 200.000000
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 261 276 4350.0 4600.0 250.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 62-79 in LWrist_speed_event is completely within zero chunk 0-276 in LWrist_vert_vel_movement_event
Last non-zero chunk 246-261 in LWrist_speed_event is completely within zero chunk 0-276 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 62-84 in RWrist_speed_event is completely within zero chunk 0-276 in RWrist_vert_vel_movement_event
Last non-zero chunk 244-266 in RWrist_speed_event is completely within zero chunk 0-276 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_50_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 130 137 2166.666667 2283.333333 116.666667
3 2 186 192 3100.000000 3200.000000 100.000000
5 3 286 292 4766.666667 4866.666667 100.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 183 193 3050.0 3216.666667 166.666667
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 95 103 1583.333333 1716.666667 133.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 185 190 3083.333333 3166.666667 83.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 92 104 1533.333333 1733.333333 200.000000
3 2 242 249 4033.333333 4150.000000 116.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 88 96 1466.666667 1600.0 133.333333
3 2 235 252 3916.666667 4200.0 283.333333
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 88-124 in LWrist_speed_event partially overlaps with zero chunk 0-91 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 85-106 in RWrist_speed_event is completely within zero chunk 0-292 in RWrist_vert_vel_movement_event
Last non-zero chunk 231-257 in RWrist_speed_event is completely within zero chunk 0-292 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_51_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 6 206 207 3433.333333 3450.0 16.666667
column
5 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 76 79 1266.666667 1316.666667 50.0
column
1 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 61 65 1016.666667 1083.333333 66.666667
column
1 RKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_52_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 9 9 150.000000 150.000000 0.000000
5 9 228 229 3800.000000 3816.666667 16.666667
7 10 265 267 4416.666667 4450.000000 33.333333
column
1 RHeel_speed_event
5 RHeel_speed_event
7 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 121 141 2016.666667 2350.0 333.333333
3 2 195 210 3250.000000 3500.0 250.000000
7 4 299 309 4983.333333 5150.0 166.666667
column
1 LElbow_speed_event
3 LElbow_speed_event
7 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 58 71 966.666667 1183.333333 216.666667
5 3 222 236 3700.000000 3933.333333 233.333333
column
1 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 69 77 1150.0 1283.333333 133.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 128 140 2133.333333 2333.333333 200.000000
3 2 197 205 3283.333333 3416.666667 133.333333
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 79-89 in LWrist_speed_event is completely within zero chunk 0-297 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 329-356 in LWrist_speed_event partially overlaps with zero chunk 353-423 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 53-87 in RWrist_speed_event is completely within zero chunk 0-135 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 259-367 in RWrist_speed_event partially overlaps with zero chunk 354-423 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 132 134 2200.000000 2233.333333 33.333333
3 3 196 208 3266.666667 3466.666667 200.000000
column
1 LWrist_speed_event
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_53_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 7 177 178 2950.000000 2966.666667 16.666667
5 8 217 233 3616.666667 3883.333333 266.666667
column
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 64 75 1066.666667 1250.0 183.333333
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 4 238 247 3966.666667 4116.666667 150.0
column
5 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 5 262 267 4366.666667 4450.0 83.333333
column
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 196 216 3266.666667 3600.0 333.333333
column
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 69 78 1150.0 1300.0 150.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 52-78 in RWrist_speed_event partially overlaps with zero chunk 0-53 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 54 78 900.0 1300.0 400.0
column
1 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_5_p0_annotated.csv
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 170 175 2833.333333 2916.666667 83.333333
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 70 81 1166.666667 1350.000000 183.333333
3 2 156 172 2600.000000 2866.666667 266.666667
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 67 77 1116.666667 1283.333333 166.666667
3 2 159 171 2650.000000 2850.000000 200.000000
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 68 77 1133.333333 1283.333333 150.0
3 2 157 166 2616.666667 2766.666667 150.0
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 175 175 2916.666667 2916.666667 0.0
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 66 76 1100.000000 1266.666667 166.666667
3 2 154 175 2566.666667 2916.666667 350.000000
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 155 158 2583.333333 2633.333333 50.0
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 97 100 1616.666667 1666.666667 50.000000
3 2 153 157 2550.000000 2616.666667 66.666667
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 69-104 in LWrist_speed_event is completely within zero chunk 0-175 in LWrist_vert_vel_movement_event
Last non-zero chunk 145-175 in LWrist_speed_event is completely within zero chunk 0-175 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 65-95 in RWrist_speed_event is completely within zero chunk 0-175 in RWrist_vert_vel_movement_event
Last non-zero chunk 137-170 in RWrist_speed_event is completely within zero chunk 0-175 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_6_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 196 203 3266.666667 3383.333333 116.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 188 200 3133.333333 3333.333333 200.0
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 77-84 in LWrist_speed_event is completely within zero chunk 0-223 in LWrist_vert_vel_movement_event
Last non-zero chunk 193-223 in LWrist_speed_event is completely within zero chunk 0-223 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 69-86 in RWrist_speed_event is completely within zero chunk 0-223 in RWrist_vert_vel_movement_event
Last non-zero chunk 191-223 in RWrist_speed_event is completely within zero chunk 0-223 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_7_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 225 226 3750.0 3766.666667 16.666667
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 50 64 833.333333 1066.666667 233.333333
3 2 218 226 3633.333333 3766.666667 133.333333
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 52 62 866.666667 1033.333333 166.666667
3 2 199 212 3316.666667 3533.333333 216.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 60 61 1000.000000 1016.666667 16.666667
3 2 221 226 3683.333333 3766.666667 83.333333
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 204 209 3400.0 3483.333333 83.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 51-66 in LWrist_speed_event is completely within zero chunk 0-226 in LWrist_vert_vel_movement_event
Last non-zero chunk 196-226 in LWrist_speed_event is completely within zero chunk 0-226 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 49-72 in RWrist_speed_event is completely within zero chunk 0-226 in RWrist_vert_vel_movement_event
Last non-zero chunk 194-226 in RWrist_speed_event is completely within zero chunk 0-226 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_8_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 119 128 1983.333333 2133.333333 150.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 121 131 2016.666667 2183.333333 166.666667
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-6 in LWrist_speed_event is completely within zero chunk 0-170 in LWrist_vert_vel_movement_event
Last non-zero chunk 120-144 in LWrist_speed_event is completely within zero chunk 0-170 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 119-147 in RWrist_speed_event is completely within zero chunk 0-170 in RWrist_vert_vel_movement_event
Last non-zero chunk 119-147 in RWrist_speed_event is completely within zero chunk 0-170 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 120 144 2000.0 2400.0 400.0
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_9_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 320 324 5333.333333 5400.0 66.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 316 324 5266.666667 5400.0 133.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We do not need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 300 311 5000.0 5183.333333 183.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 294-317 in LWrist_speed_event is completely within zero chunk 0-324 in LWrist_vert_vel_movement_event
Last non-zero chunk 294-317 in LWrist_speed_event is completely within zero chunk 0-324 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 297-320 in RWrist_speed_event is completely within zero chunk 0-324 in RWrist_vert_vel_movement_event
Last non-zero chunk 297-320 in RWrist_speed_event is completely within zero chunk 0-324 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_tpose_0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_tpose_1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_0_p0_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 92 105 1533.333333 1750.000000 216.666667
5 5 131 140 2183.333333 2333.333333 150.000000
column
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 32 38 533.333333 633.333333 100.0
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 8 10 133.333333 166.666667 33.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 2 9 33.333333 150.000000 116.666667
3 2 156 157 2600.000000 2616.666667 16.666667
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 176 188 2933.333333 3133.333333 200.0
column
3 RKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We do not need to merge
We do not need to merge
value start_idx end_idx start_time end_time duration \
3 2 73 76 1216.666667 1266.666667 50.0
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 89-159 in LWrist_speed_event partially overlaps with zero chunk 139-188 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 56-161 in RWrist_speed_event partially overlaps with zero chunk 139-188 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_100_p1_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 50 53 833.333333 883.333333 50.000000
3 2 307 314 5116.666667 5233.333333 116.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 41 54 683.333333 900.0 216.666667
3 2 194 195 3233.333333 3250.0 16.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 51 57 850.000000 950.0 100.000000
3 2 353 357 5883.333333 5950.0 66.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 41 53 683.333333 883.333333 200.000000
3 2 189 200 3150.000000 3333.333333 183.333333
5 3 239 242 3983.333333 4033.333333 50.000000
7 4 302 311 5033.333333 5183.333333 150.000000
9 5 344 362 5733.333333 6033.333333 300.000000
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
9 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 43 55 716.666667 916.666667 200.000000
3 2 349 362 5816.666667 6033.333333 216.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 32-67 in LWrist_speed_event is completely within zero chunk 0-400 in LWrist_vert_vel_movement_event
Last non-zero chunk 344-364 in LWrist_speed_event is completely within zero chunk 0-400 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-62 in RWrist_speed_event is completely within zero chunk 0-400 in RWrist_vert_vel_movement_event
Last non-zero chunk 341-364 in RWrist_speed_event is completely within zero chunk 0-400 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_101_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 7 0.0 116.666667 116.666667
column
0 LHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 6 194 196 3233.333333 3266.666667 33.333333
column
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 42 48 700.0 800.000000 100.000000
3 2 180 196 3000.0 3266.666667 266.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 40 43 666.666667 716.666667 50.0
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 180 200 3000.0 3333.333333 333.333333
column
3 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 33 41 550.0 683.333333 133.333333
3 2 183 199 3050.0 3316.666667 266.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 27-47 in LWrist_speed_event is completely within zero chunk 0-211 in LWrist_vert_vel_movement_event
Last non-zero chunk 174-199 in LWrist_speed_event is completely within zero chunk 0-211 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 28-50 in RWrist_speed_event is completely within zero chunk 0-211 in RWrist_vert_vel_movement_event
Last non-zero chunk 178-200 in RWrist_speed_event is completely within zero chunk 0-211 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_102_p1_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 40 44 666.666667 733.333333 66.666667
3 2 85 86 1416.666667 1433.333333 16.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 83 91 1383.333333 1516.666667 133.333333
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 23 33 383.333333 550.000000 166.666667
3 2 206 221 3433.333333 3683.333333 250.000000
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 29 41 483.333333 683.333333 200.000000
3 2 212 219 3533.333333 3650.000000 116.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 22 34 366.666667 566.666667 200.000000
3 2 208 222 3466.666667 3700.000000 233.333333
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 26 40 433.333333 666.666667 233.333333
3 2 210 225 3500.000000 3750.000000 250.000000
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 18-39 in LWrist_speed_event is completely within zero chunk 0-239 in LWrist_vert_vel_movement_event
Last non-zero chunk 201-223 in LWrist_speed_event is completely within zero chunk 0-239 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 17-47 in RWrist_speed_event is completely within zero chunk 0-239 in RWrist_vert_vel_movement_event
Last non-zero chunk 206-228 in RWrist_speed_event is completely within zero chunk 0-239 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_103_p1_annotated.csv
We need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 123 124 2050.0 2066.666667 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 168 172 2800.0 2866.666667 66.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 26 37 433.333333 616.666667 183.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 29 38 483.333333 633.333333 150.000000
3 2 134 136 2233.333333 2266.666667 33.333333
5 3 293 302 4883.333333 5033.333333 150.000000
column
1 RElbow_speed_event
3 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 26 37 433.333333 616.666667 183.333333
3 2 129 145 2150.000000 2416.666667 266.666667
5 3 202 219 3366.666667 3650.000000 283.333333
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 20 29 333.333333 483.333333 150.0
3 2 289 301 4816.666667 5016.666667 200.0
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 17-42 in LWrist_speed_event is completely within zero chunk 0-324 in LWrist_vert_vel_movement_event
Last non-zero chunk 286-307 in LWrist_speed_event is completely within zero chunk 0-324 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 16-38 in RWrist_speed_event is completely within zero chunk 0-324 in RWrist_vert_vel_movement_event
Last non-zero chunk 282-306 in RWrist_speed_event is completely within zero chunk 0-324 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_104_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 51 52 850.000000 866.666667 16.666667
3 2 112 133 1866.666667 2216.666667 350.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 74 82 1233.333333 1366.666667 133.333333
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 38 49 633.333333 816.666667 183.333333
3 2 118 127 1966.666667 2116.666667 150.000000
5 4 162 180 2700.000000 3000.000000 300.000000
column
1 RShoulder_speed_event
3 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 39 43 650.0 716.666667 66.666667
3 2 171 176 2850.0 2933.333333 83.333333
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 40 49 666.666667 816.666667 150.000000
3 2 83 93 1383.333333 1550.000000 166.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 33 47 550.000000 783.333333 233.333333
3 2 170 186 2833.333333 3100.000000 266.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-52 in LWrist_speed_event is completely within zero chunk 0-194 in LWrist_vert_vel_movement_event
Last non-zero chunk 166-187 in LWrist_speed_event is completely within zero chunk 0-194 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 30-54 in RWrist_speed_event is completely within zero chunk 0-194 in RWrist_vert_vel_movement_event
Last non-zero chunk 165-188 in RWrist_speed_event is completely within zero chunk 0-194 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_105_p1_annotated.csv
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 160 163 2666.666667 2716.666667 50.0
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 152 156 2533.333333 2600.0 66.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration column
3 2 159 165 2650.0 2750.0 100.0 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 35 46 583.333333 766.666667 183.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 42 56 700.0 933.333333 233.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 152 164 2533.333333 2733.333333 200.0
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 35 45 583.333333 750.0 166.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 33 40 550.000000 666.666667 116.666667
3 2 160 175 2666.666667 2916.666667 250.000000
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 26-46 in LWrist_speed_event is completely within zero chunk 0-184 in LWrist_vert_vel_movement_event
Last non-zero chunk 148-175 in LWrist_speed_event is completely within zero chunk 0-184 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 29-53 in RWrist_speed_event is completely within zero chunk 0-184 in RWrist_vert_vel_movement_event
Last non-zero chunk 156-176 in RWrist_speed_event is completely within zero chunk 0-184 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_106_p1_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 143 154 2383.333333 2566.666667 183.333333
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 147 154 2450.0 2566.666667 116.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 32 46 533.333333 766.666667 233.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 35 48 583.333333 800.000000 216.666667
3 2 141 154 2350.000000 2566.666667 216.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 140 145 2333.333333 2416.666667 83.333333
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 31 48 516.666667 800.000000 283.333333
3 2 135 154 2250.000000 2566.666667 316.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 28 40 466.666667 666.666667 200.000000
3 2 135 154 2250.000000 2566.666667 316.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 146 154 2433.333333 2566.666667 133.333333
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 25-47 in LWrist_speed_event is completely within zero chunk 0-154 in LWrist_vert_vel_movement_event
Last non-zero chunk 135-154 in LWrist_speed_event is completely within zero chunk 0-154 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 25-47 in RWrist_speed_event is completely within zero chunk 0-154 in RWrist_vert_vel_movement_event
Last non-zero chunk 132-154 in RWrist_speed_event is completely within zero chunk 0-154 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_107_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 246 254 4100.0 4233.333333 133.333333
column
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 165 172 2750.000000 2866.666667 116.666667
3 2 196 208 3266.666667 3466.666667 200.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 205 206 3416.666667 3433.333333 16.666667
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 195 207 3250.0 3450.0 200.0
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 194-218 in LWrist_speed_event is completely within zero chunk 0-266 in LWrist_vert_vel_movement_event
Last non-zero chunk 194-218 in LWrist_speed_event is completely within zero chunk 0-266 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 191-215 in RWrist_speed_event is completely within zero chunk 0-266 in RWrist_vert_vel_movement_event
Last non-zero chunk 191-215 in RWrist_speed_event is completely within zero chunk 0-266 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_108_p1_annotated.csv
We do not need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 203 204 3383.333333 3400.0 16.666667
3 2 231 231 3850.000000 3850.0 0.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 172 178 2866.666667 2966.666667 100.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 70 74 1166.666667 1233.333333 66.666667
3 2 111 114 1850.000000 1900.000000 50.000000
column
1 Head_speed_event
3 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 84 88 1400.0 1466.666667 66.666667
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 73 89 1216.666667 1483.333333 266.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 73 84 1216.666667 1400.0 183.333333
3 2 229 243 3816.666667 4050.0 233.333333
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 235 243 3916.666667 4050.0 133.333333
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 69-94 in LWrist_speed_event is completely within zero chunk 0-243 in LWrist_vert_vel_movement_event
Last non-zero chunk 225-243 in LWrist_speed_event is completely within zero chunk 0-243 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 69-93 in RWrist_speed_event is completely within zero chunk 0-243 in RWrist_vert_vel_movement_event
Last non-zero chunk 219-243 in RWrist_speed_event is completely within zero chunk 0-243 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_109_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 365 369 6083.333333 6150.0 66.666667
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 56 58 933.333333 966.666667 33.333333
3 2 94 100 1566.666667 1666.666667 100.000000
9 10 287 298 4783.333333 4966.666667 183.333333
11 11 334 338 5566.666667 5633.333333 66.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
9 RHeel_speed_event
11 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 169 170 2816.666667 2833.333333 16.666667
3 2 203 210 3383.333333 3500.000000 116.666667
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 57 66 950.000000 1100.000000 150.000000
3 2 100 107 1666.666667 1783.333333 116.666667
column
1 Head_speed_event
3 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 42 57 700.0 950.0 250.0
5 4 285 303 4750.0 5050.0 300.0
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 43 56 716.666667 933.333333 216.666667
3 2 352 369 5866.666667 6150.000000 283.333333
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 47 48 783.333333 800.000000 16.666667
3 2 353 355 5883.333333 5916.666667 33.333333
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 5 287 291 4783.333333 4850.0 66.666667
7 6 347 363 5783.333333 6050.0 266.666667
column
5 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 39 51 650.0 850.0 200.0
3 2 345 366 5750.0 6100.0 350.0
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 34-58 in LWrist_speed_event is completely within zero chunk 0-369 in LWrist_vert_vel_movement_event
Last non-zero chunk 337-365 in LWrist_speed_event is completely within zero chunk 0-369 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-60 in RWrist_speed_event is completely within zero chunk 0-369 in RWrist_vert_vel_movement_event
Last non-zero chunk 339-369 in RWrist_speed_event is completely within zero chunk 0-369 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 169 174 2816.666667 2900.0 83.333333
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_10_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 13 314 314 5233.333333 5233.333333 0.0
column
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 3 180 187 3000.000000 3116.666667 116.666667
7 4 239 243 3983.333333 4050.000000 66.666667
column
5 Head_speed_event
7 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
4 4 291 302 4850.000000 5033.333333 183.333333
6 5 341 347 5683.333333 5783.333333 100.000000
column
4 RShoulder_speed_event
6 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 16 0.0 266.666667 266.666667
4 6 345 347 5750.0 5783.333333 33.333333
column
0 LShoulder_speed_event
4 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
Last non-zero chunk 0-292 in LWrist_speed_event partially overlaps with zero chunk 280-347 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
Last non-zero chunk 345-347 in RWrist_speed_event is completely within zero chunk 277-347 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_110_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 79 94 1316.666667 1566.666667 250.000000
3 3 171 176 2850.000000 2933.333333 83.333333
5 4 201 215 3350.000000 3583.333333 233.333333
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 39 46 650.000000 766.666667 116.666667
3 2 135 137 2250.000000 2283.333333 33.333333
5 3 271 283 4516.666667 4716.666667 200.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 30 44 500.0 733.333333 233.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 90 95 1500.000000 1583.333333 83.333333
3 2 115 120 1916.666667 2000.000000 83.333333
5 3 204 224 3400.000000 3733.333333 333.333333
7 4 275 296 4583.333333 4933.333333 350.000000
column
1 RElbow_speed_event
3 RElbow_speed_event
5 RElbow_speed_event
7 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration column
1 1 39 39 650.0 650.0 0.0 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 293 302 4883.333333 5033.333333 150.0
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 29 44 483.333333 733.333333 250.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
value start_idx end_idx start_time end_time duration \
1 1 279 295 4650.0 4916.666667 266.666667
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 29 32 483.333333 533.333333 50.0
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 22-65 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event
Last non-zero chunk 268-297 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 23-44 in RWrist_speed_event is completely within zero chunk 0-302 in RWrist_vert_vel_movement_event
Last non-zero chunk 273-298 in RWrist_speed_event is completely within zero chunk 0-302 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_111_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 184 189 3066.666667 3150.000000 83.333333
3 2 338 344 5633.333333 5733.333333 100.000000
column
1 LKnee_speed_event
3 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 9 273 273 4550.000000 4550.000000 0.000000
7 10 308 310 5133.333333 5166.666667 33.333333
column
5 RHeel_speed_event
7 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 54 64 900.000000 1066.666667 166.666667
5 3 218 228 3633.333333 3800.000000 166.666667
column
1 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 50 61 833.333333 1016.666667 183.333333
3 2 107 110 1783.333333 1833.333333 50.000000
7 5 219 235 3650.000000 3916.666667 266.666667
9 6 332 344 5533.333333 5733.333333 200.000000
column
1 RShoulder_speed_event
3 RShoulder_speed_event
7 RShoulder_speed_event
9 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 61 70 1016.666667 1166.666667 150.0
column
1 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 144 146 2400.000000 2433.333333 33.333333
3 2 338 344 5633.333333 5733.333333 100.000000
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 49 62 816.666667 1033.333333 216.666667
5 4 221 237 3683.333333 3950.000000 266.666667
7 5 332 344 5533.333333 5733.333333 200.000000
column
1 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 232 241 3866.666667 4016.666667 150.000000
3 2 328 344 5466.666667 5733.333333 266.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 41-64 in LWrist_speed_event is completely within zero chunk 0-139 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 324-344 in LWrist_speed_event is completely within zero chunk 218-344 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 44-68 in RWrist_speed_event is completely within zero chunk 0-344 in RWrist_vert_vel_movement_event
Last non-zero chunk 326-344 in RWrist_speed_event is completely within zero chunk 0-344 in RWrist_vert_vel_movement_event
We need to merge
value start_idx end_idx start_time end_time duration \
1 2 220 243 3666.666667 4050.0 383.333333
column
1 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_112_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 203 215 3383.333333 3583.333333 200.0
column
1 LHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 16 25 266.666667 416.666667 150.000000
3 3 58 59 966.666667 983.333333 16.666667
7 11 222 234 3700.000000 3900.000000 200.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
7 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 243 260 4050.0 4333.333333 283.333333
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 41 55 683.333333 916.666667 233.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 54 59 900.000000 983.333333 83.333333
3 2 81 91 1350.000000 1516.666667 166.666667
5 3 163 171 2716.666667 2850.000000 133.333333
column
1 RElbow_speed_event
3 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 38 50 633.333333 833.333333 200.000000
3 2 248 264 4133.333333 4400.000000 266.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 255 262 4250.0 4366.666667 116.666667
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 31-57 in LWrist_speed_event is completely within zero chunk 0-290 in LWrist_vert_vel_movement_event
Last non-zero chunk 236-264 in LWrist_speed_event is completely within zero chunk 0-290 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 32-60 in RWrist_speed_event is completely within zero chunk 0-290 in RWrist_vert_vel_movement_event
Last non-zero chunk 221-264 in RWrist_speed_event is completely within zero chunk 0-290 in RWrist_vert_vel_movement_event
We need to merge
value start_idx end_idx start_time end_time duration \
1 2 132 143 2200.000000 2383.333333 183.333333
3 3 181 205 3016.666667 3416.666667 400.000000
column
1 RWrist_speed_event
3 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 87 89 1450.0 1483.333333 33.333333
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_113_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 170 191 2833.333333 3183.333333 350.000000
3 3 247 248 4116.666667 4133.333333 16.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 37 48 616.666667 800.0 183.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 37 47 616.666667 783.333333 166.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 35 42 583.333333 700.000000 116.666667
3 2 200 214 3333.333333 3566.666667 233.333333
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 212 214 3533.333333 3566.666667 33.333333
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 28-50 in LWrist_speed_event is completely within zero chunk 0-273 in LWrist_vert_vel_movement_event
Last non-zero chunk 198-217 in LWrist_speed_event is completely within zero chunk 0-273 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-53 in RWrist_speed_event is completely within zero chunk 0-273 in RWrist_vert_vel_movement_event
Last non-zero chunk 194-216 in RWrist_speed_event is completely within zero chunk 0-273 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
1 2 194 216 3233.333333 3600.0 366.666667
column
1 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 198 217 3300.0 3616.666667 316.666667
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_11_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 65 73 1083.333333 1216.666667 133.333333
3 3 106 110 1766.666667 1833.333333 66.666667
5 4 172 179 2866.666667 2983.333333 116.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 173 175 2883.333333 2916.666667 33.333333
column
1 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 168 185 2800.0 3083.333333 283.333333
column
1 RKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 48 55 800.000000 916.666667 116.666667
7 4 260 281 4333.333333 4683.333333 350.000000
column
1 RWrist_vert_vel_movement_event
7 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 13-78 in LWrist_speed_event partially overlaps with zero chunk 0-29 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 96-298 in LWrist_speed_event partially overlaps with zero chunk 292-329 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 18-67 in RWrist_speed_event is completely within zero chunk 0-93 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 86-300 in RWrist_speed_event partially overlaps with zero chunk 238-329 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_12_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 76 76 1266.666667 1266.666667 0.0
3 2 127 127 2116.666667 2116.666667 0.0
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 40 46 666.666667 766.666667 100.0
column
1 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 17-331 in LWrist_speed_event partially overlaps with zero chunk 0-37 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 17-331 in LWrist_speed_event partially overlaps with zero chunk 322-364 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 15-85 in RWrist_speed_event partially overlaps with zero chunk 0-39 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 97-337 in RWrist_speed_event partially overlaps with zero chunk 321-364 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_13_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 40 40 666.666667 666.666667 0.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 103 114 1716.666667 1900.0 183.333333
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 256 260 4266.666667 4333.333333 66.666667
column
3 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 79 91 1316.666667 1516.666667 200.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 41 43 683.333333 716.666667 33.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 172 194 2866.666667 3233.333333 366.666667
column
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 21-66 in LWrist_speed_event partially overlaps with zero chunk 0-25 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 171-296 in LWrist_speed_event partially overlaps with zero chunk 280-319 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 171-292 in RWrist_speed_event partially overlaps with zero chunk 279-319 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_14_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 93 104 1550.000000 1733.333333 183.333333
3 2 334 342 5566.666667 5700.000000 133.333333
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 16 23 266.666667 383.333333 116.666667
3 2 85 105 1416.666667 1750.000000 333.333333
5 3 232 234 3866.666667 3900.000000 33.333333
9 5 330 342 5500.000000 5700.000000 200.000000
column
1 RElbow_speed_event
3 RElbow_speed_event
5 RElbow_speed_event
9 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 228 242 3800.000000 4033.333333 233.333333
5 3 274 287 4566.666667 4783.333333 216.666667
column
3 RWrist_vert_vel_movement_event
5 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 73 96 1216.666667 1600.000000 383.333333
3 2 330 346 5500.000000 5766.666667 266.666667
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 18-34 in LWrist_speed_event is completely within zero chunk 0-391 in LWrist_vert_vel_movement_event
Last non-zero chunk 302-350 in LWrist_speed_event is completely within zero chunk 0-391 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 13-31 in RWrist_speed_event is completely within zero chunk 0-67 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 77 95 1283.333333 1583.333333 300.000000
3 3 242 262 4033.333333 4366.666667 333.333333
column
1 LWrist_speed_event
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_15_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 109 119 1816.666667 1983.333333 166.666667
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 37 54 616.666667 900.0 283.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 41 64 683.333333 1066.666667 383.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 39 62 650.0 1033.333333 383.333333
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 34-63 in LWrist_speed_event is completely within zero chunk 0-95 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-63 in RWrist_speed_event is completely within zero chunk 0-95 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_16_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 60 61 1000.0 1016.666667 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 86 92 1433.333333 1533.333333 100.0
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 50 71 833.333333 1183.333333 350.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 56 61 933.333333 1016.666667 83.333333
column
1 RKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 166 186 2766.666667 3100.0 333.333333
column
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 21-38 in LWrist_speed_event is completely within zero chunk 0-43 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 169-183 in LWrist_speed_event is completely within zero chunk 71-218 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 22-184 in RWrist_speed_event partially overlaps with zero chunk 0-35 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 22-184 in RWrist_speed_event partially overlaps with zero chunk 146-218 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 144 152 2400.0 2533.333333 133.333333
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_17_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 56 58 933.333333 966.666667 33.333333
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 44 56 733.333333 933.333333 200.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 178 183 2966.666667 3050.0 83.333333
column
1 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 115 121 1916.666667 2016.666667 100.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 40-47 in LWrist_speed_event is completely within zero chunk 0-95 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 17-86 in RWrist_speed_event partially overlaps with zero chunk 0-29 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 162-247 in RWrist_speed_event partially overlaps with zero chunk 245-274 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_18_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 91 91 1516.666667 1516.666667 0.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 151 151 2516.666667 2516.666667 0.0
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 201 205 3350.0 3416.666667 66.666667
column
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 22 34 366.666667 566.666667 200.0
3 2 63 81 1050.000000 1350.000000 300.0
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
3 4 287 305 4783.333333 5083.333333 300.0
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 110 121 1833.333333 2016.666667 183.333333
5 3 198 207 3300.000000 3450.000000 150.000000
column
3 LWrist_vert_vel_movement_event
5 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 320-419 in LWrist_speed_event partially overlaps with zero chunk 398-448 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 27-43 in RWrist_speed_event is completely within zero chunk 0-77 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 322-416 in RWrist_speed_event partially overlaps with zero chunk 410-448 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_19_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 116 121 1933.333333 2016.666667 83.333333
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 215 236 3583.333333 3933.333333 350.0
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 75 79 1250.0 1316.666667 66.666667
5 4 228 237 3800.0 3950.000000 150.000000
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 122 142 2033.333333 2366.666667 333.333333
3 2 221 233 3683.333333 3883.333333 200.000000
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 222 238 3700.0 3966.666667 266.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 68 81 1133.333333 1350.0 216.666667
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 63-86 in LWrist_speed_event is completely within zero chunk 0-108 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 217-249 in LWrist_speed_event partially overlaps with zero chunk 248-263 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 65-87 in RWrist_speed_event is completely within zero chunk 0-105 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We do not need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_1_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 129 137 2150.0 2283.333333 133.333333
column
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 108 115 1800.0 1916.666667 116.666667
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 5 218 222 3633.333333 3700.0 66.666667
column
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 48 62 800.000000 1033.333333 233.333333
3 2 122 132 2033.333333 2200.000000 166.666667
column
1 RKnee_speed_event
3 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 13 0.0 216.666667 216.666667
column
0 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 150 154 2500.0 2566.666667 66.666667
3 2 177 177 2950.0 2950.000000 0.000000
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 4-34 in LWrist_speed_event is completely within zero chunk 0-222 in LWrist_vert_vel_movement_event
Last non-zero chunk 133-179 in LWrist_speed_event is completely within zero chunk 0-222 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_20_p1_annotated.csv
We need to merge
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 41 51 683.333333 850.0 166.666667
3 2 80 90 1333.333333 1500.0 166.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 20 0.000000 333.333333 333.333333
2 2 133 143 2216.666667 2383.333333 166.666667
6 4 207 217 3450.000000 3616.666667 166.666667
column
0 LElbow_speed_event
2 LElbow_speed_event
6 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration column
1 1 117 126 1950.0 2100.0 150.0 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 12 0.0 200.0 200.0
column
0 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
2 2 132 145 2200.0 2416.666667 216.666667
4 3 213 230 3550.0 3833.333333 283.333333
column
2 RElbow_speed_event
4 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 22 26 366.666667 433.333333 66.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 14 0.0 233.333333 233.333333
2 2 93 109 1550.0 1816.666667 266.666667
4 3 237 241 3950.0 4016.666667 66.666667
column
0 RWrist_vert_vel_movement_event
2 RWrist_vert_vel_movement_event
4 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 17 0.000000 283.333333 283.333333
2 2 92 105 1533.333333 1750.000000 216.666667
column
0 LWrist_vert_vel_movement_event
2 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-29 in LWrist_speed_event is completely within zero chunk 0-156 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 205-247 in LWrist_speed_event partially overlaps with zero chunk 236-268 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-31 in RWrist_speed_event is completely within zero chunk 0-268 in RWrist_vert_vel_movement_event
Last non-zero chunk 220-248 in RWrist_speed_event is completely within zero chunk 0-268 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
3 3 140 148 2333.333333 2466.666667 133.333333
column
3 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_21_p1_annotated.csv
We do not need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 257 258 4283.333333 4300.0 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 274 280 4566.666667 4666.666667 100.0
column
3 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 230 242 3833.333333 4033.333333 200.0
column
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 97 106 1616.666667 1766.666667 150.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 147 154 2450.0 2566.666667 116.666667
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 221-256 in RWrist_speed_event partially overlaps with zero chunk 255-280 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
3 2 135 153 2250.0 2550.0 300.0
column
3 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_22_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 368 369 6133.333333 6150.0 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 204 225 3400.000000 3750.000000 350.0
3 2 356 377 5933.333333 6283.333333 350.0
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 25 38 416.666667 633.333333 216.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 302 308 5033.333333 5133.333333 100.0
column
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 25 43 416.666667 716.666667 300.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
We do not need to merge
value start_idx end_idx start_time end_time duration \
1 1 30 43 500.0 716.666667 216.666667
3 2 132 145 2200.0 2416.666667 216.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 26-46 in LWrist_speed_event is completely within zero chunk 0-191 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 24-45 in RWrist_speed_event is completely within zero chunk 0-190 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We do not need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_23_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 126 139 2100.0 2316.666667 216.666667
5 5 291 293 4850.0 4883.333333 33.333333
column
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 168 185 2800.0 3083.333333 283.333333
5 3 252 268 4200.0 4466.666667 266.666667
column
3 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 4 253 267 4216.666667 4450.0 233.333333
column
5 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 46 51 766.666667 850.0 83.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 98 107 1633.333333 1783.333333 150.0
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 84-110 in LWrist_speed_event is completely within zero chunk 0-145 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 231-278 in LWrist_speed_event partially overlaps with zero chunk 275-300 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 85-197 in RWrist_speed_event partially overlaps with zero chunk 0-90 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 232-281 in RWrist_speed_event partially overlaps with zero chunk 280-300 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_24_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 280 286 4666.666667 4766.666667 100.0
column
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 93 100 1550.000000 1666.666667 116.666667
3 2 260 265 4333.333333 4416.666667 83.333333
5 3 312 312 5200.000000 5200.000000 0.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 84 100 1400.0 1666.666667 266.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 286 302 4766.666667 5033.333333 266.666667
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_25_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 9 9 150.000000 150.000000 0.000000
3 2 91 113 1516.666667 1883.333333 366.666667
5 4 214 216 3566.666667 3600.000000 33.333333
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 61 69 1016.666667 1150.0 133.333333
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 252 257 4200.0 4283.333333 83.333333
column
3 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 125 131 2083.333333 2183.333333 100.000000
3 2 165 184 2750.000000 3066.666667 316.666667
5 3 262 283 4366.666667 4716.666667 350.000000
column
1 RShoulder_speed_event
3 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 56 66 933.333333 1100.000000 166.666667
5 3 164 184 2733.333333 3066.666667 333.333333
column
1 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 71 73 1183.333333 1216.666667 33.333333
3 2 124 131 2066.666667 2183.333333 116.666667
5 3 169 179 2816.666667 2983.333333 166.666667
7 4 267 280 4450.000000 4666.666667 216.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 49 65 816.666667 1083.333333 266.666667
5 3 179 200 2983.333333 3333.333333 350.000000
7 4 262 284 4366.666667 4733.333333 366.666667
column
1 RWrist_vert_vel_movement_event
5 RWrist_vert_vel_movement_event
7 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 51 66 850.000000 1100.0 250.000000
5 3 182 198 3033.333333 3300.0 266.666667
column
1 LWrist_vert_vel_movement_event
5 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 54-69 in LWrist_speed_event is completely within zero chunk 0-110 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 245-285 in LWrist_speed_event partially overlaps with zero chunk 276-307 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 53-69 in RWrist_speed_event is completely within zero chunk 0-107 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 245-290 in RWrist_speed_event is completely within zero chunk 151-307 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_26_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 78 79 1300.000000 1316.666667 16.666667
3 2 171 190 2850.000000 3166.666667 316.666667
5 4 244 248 4066.666667 4133.333333 66.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 308 316 5133.333333 5266.666667 133.333333
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 79 92 1316.666667 1533.333333 216.666667
3 2 180 194 3000.000000 3233.333333 233.333333
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 47 60 783.333333 1000.000000 216.666667
5 3 174 177 2900.000000 2950.000000 50.000000
9 5 322 332 5366.666667 5533.333333 166.666667
column
1 RElbow_speed_event
5 RElbow_speed_event
9 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 186 189 3100.000000 3150.000000 50.0
3 2 265 268 4416.666667 4466.666667 50.0
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 82 99 1366.666667 1650.0 283.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 41 50 683.333333 833.333333 150.0
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 43 46 716.666667 766.666667 50.000000
3 2 64 83 1066.666667 1383.333333 316.666667
7 4 308 309 5133.333333 5150.000000 16.666667
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
7 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 61-88 in LWrist_speed_event is completely within zero chunk 0-240 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 53-53 in RWrist_speed_event is completely within zero chunk 0-84 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 318-332 in RWrist_speed_event is completely within zero chunk 284-332 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_27_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
0 1 0 13 0.0 216.666667 216.666667
column
0 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 2 13 33.333333 216.666667 183.333333
3 3 81 93 1350.000000 1550.000000 200.000000
7 6 272 273 4533.333333 4550.000000 16.666667
9 7 298 308 4966.666667 5133.333333 166.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
7 RHeel_speed_event
9 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 98 113 1633.333333 1883.333333 250.000000
3 2 268 271 4466.666667 4516.666667 50.000000
5 3 348 352 5800.000000 5866.666667 66.666667
column
1 LElbow_speed_event
3 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 4 180 181 3000.0 3016.666667 16.666667
column
5 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 174 191 2900.000000 3183.333333 283.333333
5 5 251 263 4183.333333 4383.333333 200.000000
7 6 302 323 5033.333333 5383.333333 350.000000
column
3 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 347 352 5783.333333 5866.666667 83.333333
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 49-98 in LWrist_speed_event is completely within zero chunk 0-352 in LWrist_vert_vel_movement_event
Last non-zero chunk 328-333 in LWrist_speed_event is completely within zero chunk 0-352 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_28_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 8 350 355 5833.333333 5916.666667 83.333333
column
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 97 112 1616.666667 1866.666667 250.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 3 253 258 4216.666667 4300.0 83.333333
column
5 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 204 219 3400.0 3650.0 250.0
column
3 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 205 224 3416.666667 3733.333333 316.666667
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 266 288 4433.333333 4800.0 366.666667
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 43-62 in LWrist_speed_event is completely within zero chunk 0-363 in LWrist_vert_vel_movement_event
Last non-zero chunk 264-286 in LWrist_speed_event is completely within zero chunk 0-363 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 45-144 in RWrist_speed_event partially overlaps with zero chunk 0-75 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 255-304 in RWrist_speed_event partially overlaps with zero chunk 301-363 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
3 2 204 226 3400.0 3766.666667 366.666667
column
3 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 264 286 4400.0 4766.666667 366.666667
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_29_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 2 14 33.333333 233.333333 200.0
5 6 226 244 3766.666667 4066.666667 300.0
column
1 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 62 67 1033.333333 1116.666667 83.333333
3 2 130 136 2166.666667 2266.666667 100.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 4 255 262 4250.0 4366.666667 116.666667
column
5 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 56 69 933.333333 1150.000000 216.666667
5 4 245 262 4083.333333 4366.666667 283.333333
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 5 243 262 4050.0 4366.666667 316.666667
column
3 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 54 64 900.000000 1066.666667 166.666667
3 2 242 262 4033.333333 4366.666667 333.333333
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 46-66 in LWrist_speed_event is completely within zero chunk 0-262 in LWrist_vert_vel_movement_event
Last non-zero chunk 238-262 in LWrist_speed_event is completely within zero chunk 0-262 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 48-70 in RWrist_speed_event is completely within zero chunk 0-262 in RWrist_vert_vel_movement_event
Last non-zero chunk 237-262 in RWrist_speed_event is completely within zero chunk 0-262 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_2_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 25 27 416.666667 450.000000 33.333333
3 2 97 98 1616.666667 1633.333333 16.666667
5 3 192 193 3200.000000 3216.666667 16.666667
9 8 330 344 5500.000000 5733.333333 233.333333
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
9 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 5 335 335 5583.333333 5583.333333 0.0
column
5 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 42 58 700.000000 966.666667 266.666667
7 5 401 403 6683.333333 6716.666667 33.333333
column
1 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 5-17 in LWrist_speed_event is completely within zero chunk 0-99 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 282-353 in RWrist_speed_event partially overlaps with zero chunk 348-403 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 31 32 516.666667 533.333333 16.666667
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_30_p1_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 332 332 5533.333333 5533.333333 0.0
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 173 189 2883.333333 3150.000000 266.666667
3 3 232 233 3866.666667 3883.333333 16.666667
5 4 284 284 4733.333333 4733.333333 0.000000
7 5 310 311 5166.666667 5183.333333 16.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
7 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
5 4 316 332 5266.666667 5533.333333 266.666667
column
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 271 280 4516.666667 4666.666667 150.0
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 71 72 1183.333333 1200.0 16.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 71 73 1183.333333 1216.666667 33.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 66 79 1100.0 1316.666667 216.666667
3 2 321 332 5350.0 5533.333333 183.333333
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
5 4 329 332 5483.333333 5533.333333 50.0
column
5 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 58-83 in LWrist_speed_event is completely within zero chunk 0-164 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 313-332 in LWrist_speed_event is completely within zero chunk 282-332 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 61-84 in RWrist_speed_event is completely within zero chunk 0-332 in RWrist_vert_vel_movement_event
Last non-zero chunk 315-332 in RWrist_speed_event is completely within zero chunk 0-332 in RWrist_vert_vel_movement_event
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_31_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
3 2 433 433 7216.666667 7216.666667 0.0
column
3 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 103 105 1716.666667 1750.0 33.333333
5 6 240 240 4000.000000 4000.0 0.000000
column
1 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 177 193 2950.000000 3216.666667 266.666667
3 2 344 365 5733.333333 6083.333333 350.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 348 353 5800.0 5883.333333 83.333333
column
3 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 62 69 1033.333333 1150.000000 116.666667
5 4 405 418 6750.000000 6966.666667 216.666667
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 62 68 1033.333333 1133.333333 100.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
value start_idx end_idx start_time end_time duration \
1 1 60 73 1000.000000 1216.666667 216.666667
3 2 410 425 6833.333333 7083.333333 250.000000
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 63 71 1050.000000 1183.333333 133.333333
3 2 187 191 3116.666667 3183.333333 66.666667
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 54-78 in LWrist_speed_event is completely within zero chunk 0-336 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 386-423 in LWrist_speed_event partially overlaps with zero chunk 414-433 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 54-77 in RWrist_speed_event is completely within zero chunk 0-433 in RWrist_vert_vel_movement_event
Last non-zero chunk 405-433 in RWrist_speed_event is completely within zero chunk 0-433 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
1 2 180 193 3000.0 3216.666667 216.666667
column
1 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 184 192 3066.666667 3200.0 133.333333
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_32_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 2 9 33.333333 150.000000 116.666667
3 2 89 104 1483.333333 1733.333333 250.000000
5 4 147 148 2450.000000 2466.666667 16.666667
9 11 279 300 4650.000000 5000.000000 350.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
9 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 108 112 1800.000000 1866.666667 66.666667
3 2 142 154 2366.666667 2566.666667 200.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 157 162 2616.666667 2700.0 83.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 104 112 1733.333333 1866.666667 133.333333
3 2 294 300 4900.000000 5000.000000 100.000000
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 127 131 2116.666667 2183.333333 66.666667
3 2 152 159 2533.333333 2650.000000 116.666667
5 3 298 300 4966.666667 5000.000000 33.333333
column
1 RKnee_speed_event
3 RKnee_speed_event
5 RKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 187 188 3116.666667 3133.333333 16.666667
3 2 244 265 4066.666667 4416.666667 350.000000
5 3 283 300 4716.666667 5000.000000 283.333333
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
5 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 244 260 4066.666667 4333.333333 266.666667
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 59-78 in LWrist_speed_event is completely within zero chunk 0-300 in LWrist_vert_vel_movement_event
Last non-zero chunk 281-300 in LWrist_speed_event is completely within zero chunk 0-300 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 64-83 in RWrist_speed_event is completely within zero chunk 0-300 in RWrist_vert_vel_movement_event
Last non-zero chunk 186-300 in RWrist_speed_event is completely within zero chunk 0-300 in RWrist_vert_vel_movement_event
We need to merge
value start_idx end_idx start_time end_time duration \
1 2 134 137 2233.333333 2283.333333 50.0
column
1 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 107 109 1783.333333 1816.666667 33.333333
3 3 174 190 2900.000000 3166.666667 266.666667
column
1 LWrist_speed_event
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_33_p1_annotated.csv
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 210 220 3500.0 3666.666667 166.666667
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 63 67 1050.0 1116.666667 66.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 66 75 1100.0 1250.0 150.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 3 163 169 2716.666667 2816.666667 100.0
column
5 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 63 66 1050.0 1100.0 50.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 211 220 3516.666667 3666.666667 150.0
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 194 209 3233.333333 3483.333333 250.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 215 220 3583.333333 3666.666667 83.333333
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 53-77 in LWrist_speed_event is completely within zero chunk 0-220 in LWrist_vert_vel_movement_event
Last non-zero chunk 192-220 in LWrist_speed_event is completely within zero chunk 0-220 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 54-122 in RWrist_speed_event partially overlaps with zero chunk 0-59 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 99 109 1650.0 1816.666667 166.666667
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_34_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 232 242 3866.666667 4033.333333 166.666667
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 225 228 3750.0 3800.0 50.0
column
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 223 242 3716.666667 4033.333333 316.666667
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 190 193 3166.666667 3216.666667 50.0
5 3 239 242 3983.333333 4033.333333 50.0
column
3 Head_speed_event
5 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 223 224 3716.666667 3733.333333 16.666667
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 65 83 1083.333333 1383.333333 300.0
3 2 123 126 2050.000000 2100.000000 50.0
5 3 224 242 3733.333333 4033.333333 300.0
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
0 1 0 1 0.0 16.666667 16.666667
column
0 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 63-120 in LWrist_speed_event is completely within zero chunk 0-242 in LWrist_vert_vel_movement_event
Last non-zero chunk 220-242 in LWrist_speed_event is completely within zero chunk 0-242 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-11 in RWrist_speed_event is completely within zero chunk 0-55 in RWrist_vert_vel_movement_event
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_35_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 15 16 250.000000 266.666667 16.666667
3 2 101 108 1683.333333 1800.000000 116.666667
7 7 271 275 4516.666667 4583.333333 66.666667
9 8 309 309 5150.000000 5150.000000 0.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
7 RHeel_speed_event
9 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 308 325 5133.333333 5416.666667 283.333333
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 147 153 2450.000000 2550.0 100.000000
7 4 279 282 4650.000000 4700.0 50.000000
9 5 322 327 5366.666667 5450.0 83.333333
column
3 Head_speed_event
7 Head_speed_event
9 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 201 215 3350.0 3583.333333 233.333333
column
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration column
1 1 321 327 5350.0 5450.0 100.0 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 267 277 4450.0 4616.666667 166.666667
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 172 172 2866.666667 2866.666667 0.0
column
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
5 4 226 233 3766.666667 3883.333333 116.666667
7 5 281 304 4683.333333 5066.666667 383.333333
9 6 327 327 5450.000000 5450.000000 0.000000
column
5 RWrist_vert_vel_movement_event
7 RWrist_vert_vel_movement_event
9 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 149 170 2483.333333 2833.333333 350.000000
5 4 281 298 4683.333333 4966.666667 283.333333
7 5 320 327 5333.333333 5450.000000 116.666667
column
1 LWrist_vert_vel_movement_event
5 LWrist_vert_vel_movement_event
7 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 70-94 in LWrist_speed_event is completely within zero chunk 0-197 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 279-327 in LWrist_speed_event is completely within zero chunk 247-327 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 74-119 in RWrist_speed_event partially overlaps with zero chunk 0-75 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 284-325 in RWrist_speed_event is completely within zero chunk 204-327 in RWrist_vert_vel_movement_event
We need to merge
value start_idx end_idx start_time end_time duration \
3 5 228 236 3800.0 3933.333333 133.333333
column
3 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 108 115 1800.0 1916.666667 116.666667
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_36_p1_annotated.csv
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 9 9 150.0 150.0 0.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 73 86 1216.666667 1433.333333 216.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 52 69 866.666667 1150.000000 283.333333
3 2 117 131 1950.000000 2183.333333 233.333333
5 3 179 184 2983.333333 3066.666667 83.333333
column
1 RShoulder_speed_event
3 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 126 144 2100.0 2400.0 300.0
column
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 60 65 1000.0 1083.333333 83.333333
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 51 69 850.0 1150.0 300.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
value start_idx end_idx start_time end_time duration \
3 2 114 131 1900.0 2183.333333 283.333333
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 233 256 3883.333333 4266.666667 383.333333
column
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 46-93 in LWrist_speed_event partially overlaps with zero chunk 0-65 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 235-256 in LWrist_speed_event is completely within zero chunk 95-256 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 47-99 in RWrist_speed_event partially overlaps with zero chunk 0-64 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_37_p1_annotated.csv
We do not need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 57 68 950.0 1133.333333 183.333333
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 76 92 1266.666667 1533.333333 266.666667
3 4 173 193 2883.333333 3216.666667 333.333333
5 6 300 311 5000.000000 5183.333333 183.333333
7 8 354 373 5900.000000 6216.666667 316.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
7 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 391 391 6516.666667 6516.666667 0.0
column
1 RHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 74 77 1233.333333 1283.333333 50.0
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 179 182 2983.333333 3033.333333 50.0
column
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 343 350 5716.666667 5833.333333 116.666667
3 2 376 391 6266.666667 6516.666667 250.000000
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 165 177 2750.0 2950.0 200.0
column
3 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 160 181 2666.666667 3016.666667 350.000000
7 5 374 385 6233.333333 6416.666667 183.333333
column
3 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
5 5 307 309 5116.666667 5150.0 33.333333
column
5 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
5 5 295 301 4916.666667 5016.666667 100.0
column
5 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_38_p0_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 36 36 600.0 600.0 0.0
7 13 423 429 7050.0 7150.0 100.0
column
1 RHeel_speed_event
7 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
7 7 342 356 5700.000000 5933.333333 233.333333
9 8 392 399 6533.333333 6650.000000 116.666667
11 9 419 434 6983.333333 7233.333333 250.000000
13 10 456 469 7600.000000 7816.666667 216.666667
column
7 LElbow_speed_event
9 LElbow_speed_event
11 LElbow_speed_event
13 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 343 347 5716.666667 5783.333333 66.666667
column
1 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 6 350 361 5833.333333 6016.666667 183.333333
column
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 64 67 1066.666667 1116.666667 50.0
7 13 446 449 7433.333333 7483.333333 50.0
column
1 LHip_speed_event
7 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 8 361 369 6016.666667 6150.0 133.333333
column
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 36-55 in LWrist_speed_event is completely within zero chunk 0-58 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 426-469 in LWrist_speed_event partially overlaps with zero chunk 460-469 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 35-293 in RWrist_speed_event partially overlaps with zero chunk 0-47 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 459-469 in RWrist_speed_event is completely within zero chunk 448-469 in RWrist_vert_vel_movement_event
We do not need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 6 343 358 5716.666667 5966.666667 250.000000
5 7 394 407 6566.666667 6783.333333 216.666667
column
3 LWrist_speed_event
5 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_39_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
0 1 0 2 0.0 33.333333 33.333333
column
0 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 86 95 1433.333333 1583.333333 150.0
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 9 20 150.0 333.333333 183.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 190 198 3166.666667 3300.0 133.333333
column
3 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 9 22 150.000000 366.666667 216.666667
7 5 373 386 6216.666667 6433.333333 216.666667
column
1 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
7 4 331 352 5516.666667 5866.666667 350.0
column
7 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 12-25 in LWrist_speed_event is completely within zero chunk 0-49 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 331-374 in LWrist_speed_event is completely within zero chunk 300-407 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 11-28 in RWrist_speed_event is completely within zero chunk 0-47 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 324-383 in RWrist_speed_event partially overlaps with zero chunk 372-407 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 99 114 1650.0 1900.0 250.0
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_3_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
4 3 143 153 2383.333333 2550.0 166.666667
column
4 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 146 153 2433.333333 2550.0 116.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
2 3 142 153 2366.666667 2550.0 183.333333
column
2 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 151 153 2516.666667 2550.0 33.333333
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 146 153 2433.333333 2550.0 116.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
2 3 104 108 1733.333333 1800.0 66.666667
column
2 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
no overlap
Last non-zero chunk 148-153 in LWrist_speed_event is completely within zero chunk 75-153 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
Last non-zero chunk 145-153 in RWrist_speed_event is completely within zero chunk 104-153 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_40_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
0 1 0 11 0.0 183.333333 183.333333
column
0 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 224 225 3733.333333 3750.000000 16.666667
3 2 289 292 4816.666667 4866.666667 50.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 9 9 150.000000 150.000000 0.0
9 6 287 290 4783.333333 4833.333333 50.0
column
1 LElbow_speed_event
9 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 21 34 350.0 566.666667 216.666667
5 4 171 183 2850.0 3050.000000 200.000000
7 5 204 212 3400.0 3533.333333 133.333333
9 6 231 242 3850.0 4033.333333 183.333333
column
1 RElbow_speed_event
5 RElbow_speed_event
7 RElbow_speed_event
9 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 2 0.000000 33.333333 33.333333
2 2 312 319 5200.000000 5316.666667 116.666667
4 3 352 367 5866.666667 6116.666667 250.000000
column
0 RKnee_speed_event
2 RKnee_speed_event
4 RKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We do not need to merge
We do not need to merge
value start_idx end_idx start_time end_time duration \
0 1 0 20 0.000000 333.333333 333.333333
2 2 56 80 933.333333 1333.333333 400.000000
column
0 RWrist_vert_vel_movement_event
2 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 6 24 100.000000 400.000000 300.0
3 2 59 83 983.333333 1383.333333 400.0
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 10-24 in LWrist_speed_event is completely within zero chunk 0-155 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 323-367 in LWrist_speed_event partially overlaps with zero chunk 364-367 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-13 in RWrist_speed_event is completely within zero chunk 0-156 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 324-367 in RWrist_speed_event partially overlaps with zero chunk 358-367 in RWrist_vert_vel_movement_event
We need to merge
We do not need to merge
value start_idx end_idx start_time end_time duration \
1 2 23 32 383.333333 533.333333 150.000000
3 3 94 108 1566.666667 1800.000000 233.333333
7 6 230 251 3833.333333 4183.333333 350.000000
column
1 RWrist_speed_event
3 RWrist_speed_event
7 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 66 79 1100.000000 1316.666667 216.666667
3 3 170 190 2833.333333 3166.666667 333.333333
column
1 LWrist_speed_event
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_41_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
3 3 274 291 4566.666667 4850.0 283.333333
column
3 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 166 176 2766.666667 2933.333333 166.666667
3 2 217 220 3616.666667 3666.666667 50.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 286 291 4766.666667 4850.0 83.333333
column
3 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 153 157 2550.0 2616.666667 66.666667
column
1 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
5 3 287 291 4783.333333 4850.0 66.666667
column
5 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 71 81 1183.333333 1350.000000 166.666667
3 3 128 136 2133.333333 2266.666667 133.333333
5 4 167 188 2783.333333 3133.333333 350.000000
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 33-284 in LWrist_speed_event partially overlaps with zero chunk 0-54 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 33-284 in LWrist_speed_event partially overlaps with zero chunk 280-291 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-45 in RWrist_speed_event is completely within zero chunk 0-54 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 49-287 in RWrist_speed_event partially overlaps with zero chunk 275-291 in RWrist_vert_vel_movement_event
We do not need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_43_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 29 32 483.333333 533.333333 50.000000
5 4 227 232 3783.333333 3866.666667 83.333333
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 26 32 433.333333 533.333333 100.0
3 2 72 78 1200.000000 1300.000000 100.0
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 28-41 in LWrist_speed_event is completely within zero chunk 0-126 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 237-341 in LWrist_speed_event partially overlaps with zero chunk 310-352 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 27-39 in RWrist_speed_event is completely within zero chunk 0-59 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 349-352 in RWrist_speed_event is completely within zero chunk 315-352 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_44_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 37 52 616.666667 866.666667 250.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 172 174 2866.666667 2900.0 33.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 35 45 583.333333 750.0 166.666667
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 69 87 1150.000000 1450.000000 300.000000
3 2 152 160 2533.333333 2666.666667 133.333333
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
3 3 227 241 3783.333333 4016.666667 233.333333
5 4 260 275 4333.333333 4583.333333 250.000000
column
3 RWrist_vert_vel_movement_event
5 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 36-53 in LWrist_speed_event is completely within zero chunk 0-70 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 335-341 in LWrist_speed_event is completely within zero chunk 216-364 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 34-48 in RWrist_speed_event is completely within zero chunk 0-72 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 227-350 in RWrist_speed_event is completely within zero chunk 193-364 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 5 265 276 4416.666667 4600.0 183.333333
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_45_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 340 340 5666.666667 5666.666667 0.0
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 234 235 3900.0 3916.666667 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 325 333 5416.666667 5550.0 133.333333
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 191 203 3183.333333 3383.333333 200.0
column
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 137 139 2283.333333 2316.666667 33.333333
column
1 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
5 3 278 291 4633.333333 4850.0 216.666667
column
5 RKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 140 160 2333.333333 2666.666667 333.333333
5 3 217 235 3616.666667 3916.666667 300.000000
column
3 LWrist_vert_vel_movement_event
5 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 33-178 in LWrist_speed_event partially overlaps with zero chunk 0-56 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 316-340 in LWrist_speed_event is completely within zero chunk 97-340 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-47 in RWrist_speed_event is completely within zero chunk 0-57 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 333-340 in RWrist_speed_event is completely within zero chunk 320-340 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 220 241 3666.666667 4016.666667 350.0
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_46_p0_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 420 437 7000.0 7283.333333 283.333333
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 93 95 1550.000000 1583.333333 33.333333
3 2 197 200 3283.333333 3333.333333 50.000000
5 3 243 244 4050.000000 4066.666667 16.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 437 437 7283.333333 7283.333333 0.0
column
1 RHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 345 349 5750.0 5816.666667 66.666667
column
3 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 71 92 1183.333333 1533.333333 350.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 266 273 4433.333333 4550.000000 116.666667
3 2 427 437 7116.666667 7283.333333 166.666667
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 86 95 1433.333333 1583.333333 150.000000
5 4 288 303 4800.000000 5050.000000 250.000000
7 5 418 437 6966.666667 7283.333333 316.666667
column
1 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 8-19 in LWrist_speed_event is completely within zero chunk 0-78 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 5-20 in RWrist_speed_event is completely within zero chunk 0-79 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_47_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 36 49 600.0 816.666667 216.666667
3 2 222 225 3700.0 3750.000000 50.000000
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 204 225 3400.0 3750.0 350.0
column
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 36 49 600.0 816.666667 216.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 37-53 in LWrist_speed_event is completely within zero chunk 0-225 in LWrist_vert_vel_movement_event
Last non-zero chunk 219-225 in LWrist_speed_event is completely within zero chunk 0-225 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 39-87 in RWrist_speed_event partially overlaps with zero chunk 0-55 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_48_p0_annotated.csv
We do not need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 51 52 850.0 866.666667 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
5 3 304 312 5066.666667 5200.0 133.333333
column
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 303 306 5050.0 5100.0 50.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 124 135 2066.666667 2250.000000 183.333333
5 4 169 187 2816.666667 3116.666667 300.000000
7 5 208 223 3466.666667 3716.666667 250.000000
column
3 RElbow_speed_event
5 RElbow_speed_event
7 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 40 51 666.666667 850.0 183.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 3 289 298 4816.666667 4966.666667 150.000000
7 4 319 323 5316.666667 5383.333333 66.666667
column
5 LWrist_vert_vel_movement_event
7 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 38-51 in LWrist_speed_event is completely within zero chunk 0-152 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 289-307 in LWrist_speed_event is completely within zero chunk 259-323 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 37-89 in RWrist_speed_event partially overlaps with zero chunk 0-54 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
We need to merge
value start_idx end_idx start_time end_time duration \
3 2 127 135 2116.666667 2250.0 133.333333
5 3 173 183 2883.333333 3050.0 166.666667
7 4 214 216 3566.666667 3600.0 33.333333
column
3 RWrist_speed_event
5 RWrist_speed_event
7 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_49_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 34 49 566.666667 816.666667 250.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 196 204 3266.666667 3400.0 133.333333
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 42 49 700.0 816.666667 116.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 102 111 1700.0 1850.0 150.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 38-49 in LWrist_speed_event is completely within zero chunk 0-204 in LWrist_vert_vel_movement_event
Last non-zero chunk 184-192 in LWrist_speed_event is completely within zero chunk 0-204 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 37-83 in RWrist_speed_event partially overlaps with zero chunk 0-47 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_4_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
7 6 480 486 8000.000000 8100.000000 100.000000
9 7 517 529 8616.666667 8816.666667 200.000000
11 8 557 570 9283.333333 9500.000000 216.666667
column
7 LElbow_speed_event
9 LElbow_speed_event
11 LElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 380 387 6333.333333 6450.0 116.666667
column
1 RHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 30 37 500.0 616.666667 116.666667
5 4 558 568 9300.0 9466.666667 166.666667
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 26 33 433.333333 550.0 116.666667
7 8 555 564 9250.000000 9400.0 150.000000
column
1 RElbow_speed_event
7 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 34 34 566.666667 566.666667 0.0
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 28 40 466.666667 666.666667 200.000000
3 2 171 178 2850.000000 2966.666667 116.666667
5 3 220 235 3666.666667 3916.666667 250.000000
7 4 269 277 4483.333333 4616.666667 133.333333
11 7 558 566 9300.000000 9433.333333 133.333333
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
11 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-42 in LWrist_speed_event is completely within zero chunk 0-182 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 542-567 in LWrist_speed_event is completely within zero chunk 529-600 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 26-45 in RWrist_speed_event is completely within zero chunk 0-186 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 548-568 in RWrist_speed_event is completely within zero chunk 529-600 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_50_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 63 70 1050.000000 1166.666667 116.666667
3 2 160 164 2666.666667 2733.333333 66.666667
column
1 LKnee_speed_event
3 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 140 153 2333.333333 2550.0 216.666667
column
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 163 170 2716.666667 2833.333333 116.666667
3 2 233 243 3883.333333 4050.000000 166.666667
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 32-45 in LWrist_speed_event is completely within zero chunk 0-332 in LWrist_vert_vel_movement_event
Last non-zero chunk 32-45 in LWrist_speed_event is completely within zero chunk 0-332 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 30-121 in RWrist_speed_event partially overlaps with zero chunk 0-42 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_51_p0_annotated.csv
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 6 223 223 3716.666667 3716.666667 0.0
column
5 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 131 137 2183.333333 2283.333333 100.0
3 2 283 298 4716.666667 4966.666667 250.0
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 27 33 450.0 550.0 100.0
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 82 92 1366.666667 1533.333333 166.666667
3 2 123 144 2050.000000 2400.000000 350.000000
5 3 175 183 2916.666667 3050.000000 133.333333
7 4 292 295 4866.666667 4916.666667 50.000000
9 5 384 392 6400.000000 6533.333333 133.333333
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
9 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 28-40 in LWrist_speed_event is completely within zero chunk 0-59 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 386-393 in LWrist_speed_event is completely within zero chunk 306-426 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 24-40 in RWrist_speed_event is completely within zero chunk 0-60 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We do not need to merge
value start_idx end_idx start_time end_time duration \
3 3 249 256 4150.0 4266.666667 116.666667
column
3 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
5 4 333 333 5550.0 5550.0 0.0
column
5 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_52_p0_annotated.csv
We need to merge
We need to merge
We do not need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 444 452 7400.0 7533.333333 133.333333
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 29 30 483.333333 500.000000 16.666667
3 2 198 199 3300.000000 3316.666667 16.666667
5 3 372 376 6200.000000 6266.666667 66.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 151 162 2516.666667 2700.000000 183.333333
5 3 286 290 4766.666667 4833.333333 66.666667
column
1 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration column
3 3 162 165 2700.0 2750.0 50.0 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 27 37 450.0 616.666667 166.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 28 33 466.666667 550.000000 83.333333
5 5 316 332 5266.666667 5533.333333 266.666667
column
1 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 340 357 5666.666667 5950.000000 283.333333
3 2 438 452 7300.000000 7533.333333 233.333333
column
1 RKnee_speed_event
3 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 29 34 483.333333 566.666667 83.333333
3 2 151 163 2516.666667 2716.666667 200.000000
5 3 221 235 3683.333333 3916.666667 233.333333
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-47 in LWrist_speed_event is completely within zero chunk 0-221 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 405-445 in LWrist_speed_event partially overlaps with zero chunk 443-452 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-41 in RWrist_speed_event is completely within zero chunk 0-79 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We do not need to merge
value start_idx end_idx start_time end_time duration \
5 5 320 329 5333.333333 5483.333333 150.0
column
5 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_53_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 231 232 3850.0 3866.666667 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 284 293 4733.333333 4883.333333 150.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 55 69 916.666667 1150.0 233.333333
5 4 374 387 6233.333333 6450.0 216.666667
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 367 383 6116.666667 6383.333333 266.666667
column
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 57 68 950.000000 1133.333333 183.333333
5 4 382 382 6366.666667 6366.666667 0.000000
column
1 LShoulder_speed_event
5 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 57 71 950.0 1183.333333 233.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 49-73 in LWrist_speed_event is completely within zero chunk 0-405 in LWrist_vert_vel_movement_event
Last non-zero chunk 376-398 in LWrist_speed_event is completely within zero chunk 0-405 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 51-74 in RWrist_speed_event is completely within zero chunk 0-261 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 281 300 4683.333333 5000.0 316.666667
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_54_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 431 445 7183.333333 7416.666667 233.333333
column
1 LHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 96 106 1600.000000 1766.666667 166.666667
3 3 163 163 2716.666667 2716.666667 0.000000
5 4 241 257 4016.666667 4283.333333 266.666667
7 6 338 347 5633.333333 5783.333333 150.000000
9 7 386 396 6433.333333 6600.000000 166.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
7 RHeel_speed_event
9 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 132 144 2200.000000 2400.000000 200.000000
3 2 196 215 3266.666667 3583.333333 316.666667
5 3 335 354 5583.333333 5900.000000 316.666667
9 5 441 445 7350.000000 7416.666667 66.666667
column
1 LElbow_speed_event
3 LElbow_speed_event
5 LElbow_speed_event
9 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 46 55 766.666667 916.666667 150.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 356 357 5933.333333 5950.0 16.666667
column
1 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 374 385 6233.333333 6416.666667 183.333333
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 47 56 783.333333 933.333333 150.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 45 56 750.0 933.333333 183.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
5 3 395 403 6583.333333 6716.666667 133.333333
column
5 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 37-58 in LWrist_speed_event is completely within zero chunk 0-124 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 384-421 in LWrist_speed_event is completely within zero chunk 217-445 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 39-60 in RWrist_speed_event is completely within zero chunk 0-199 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 380-425 in RWrist_speed_event partially overlaps with zero chunk 422-445 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_55_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 255 259 4250.0 4316.666667 66.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 499 499 8316.666667 8316.666667 0.0
column
3 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 406 413 6766.666667 6883.333333 116.666667
5 4 444 452 7400.000000 7533.333333 133.333333
column
3 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 103 111 1716.666667 1850.0 133.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 261 268 4350.000000 4466.666667 116.666667
3 2 309 309 5150.000000 5150.000000 0.000000
5 3 409 412 6816.666667 6866.666667 50.000000
column
1 LHip_speed_event
3 LHip_speed_event
5 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 402 417 6700.0 6950.0 250.0
column
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 93 110 1550.000000 1833.333333 283.333333
7 4 409 427 6816.666667 7116.666667 300.000000
column
1 RWrist_vert_vel_movement_event
7 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 98 100 1633.333333 1666.666667 33.333333
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 86-112 in LWrist_speed_event is completely within zero chunk 0-321 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 467-494 in LWrist_speed_event is completely within zero chunk 428-499 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 88-115 in RWrist_speed_event is completely within zero chunk 0-145 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 468-498 in RWrist_speed_event is completely within zero chunk 324-499 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 253 274 4216.666667 4566.666667 350.0
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_56_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 74 78 1233.333333 1300.000000 66.666667
3 2 115 121 1916.666667 2016.666667 100.000000
5 4 202 210 3366.666667 3500.000000 133.333333
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 132 140 2200.000000 2333.333333 133.333333
3 2 203 210 3383.333333 3500.000000 116.666667
column
1 Head_speed_event
3 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 72 74 1200.000000 1233.333333 33.333333
3 2 124 137 2066.666667 2283.333333 216.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 71 78 1183.333333 1300.0 116.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 16-52 in LWrist_speed_event partially overlaps with zero chunk 0-18 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 183-210 in LWrist_speed_event is completely within zero chunk 70-210 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 17-51 in RWrist_speed_event partially overlaps with zero chunk 0-39 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_57_p1_annotated.csv
We do not need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 169 169 2816.666667 2816.666667 0.0
3 2 225 225 3750.000000 3750.000000 0.0
5 3 290 296 4833.333333 4933.333333 100.0
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 148 166 2466.666667 2766.666667 300.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 230 238 3833.333333 3966.666667 133.333333
column
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 81 88 1350.0 1466.666667 116.666667
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 223 238 3716.666667 3966.666667 250.0
column
3 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
value start_idx end_idx start_time end_time duration \
1 1 66 83 1100.0 1383.333333 283.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 22-23 in LWrist_speed_event is completely within zero chunk 0-132 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 278-314 in LWrist_speed_event partially overlaps with zero chunk 309-316 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 21-43 in RWrist_speed_event is completely within zero chunk 0-130 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
We do not need to merge
value start_idx end_idx start_time end_time duration \
1 2 63 87 1050.0 1450.0 400.0
column
1 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 34 44 566.666667 733.333333 166.666667
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_58_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 97 98 1616.666667 1633.333333 16.666667
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 20 23 333.333333 383.333333 50.000000
3 2 161 183 2683.333333 3050.000000 366.666667
5 4 274 279 4566.666667 4650.000000 83.333333
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 200 205 3333.333333 3416.666667 83.333333
5 4 234 251 3900.000000 4183.333333 283.333333
column
3 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 59 67 983.333333 1116.666667 133.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 163 167 2716.666667 2783.333333 66.666667
3 2 240 245 4000.000000 4083.333333 83.333333
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 9 0.0 150.000000 150.000000
4 8 309 320 5150.0 5333.333333 183.333333
column
0 LShoulder_speed_event
4 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 55 62 916.666667 1033.333333 116.666667
3 2 152 158 2533.333333 2633.333333 100.000000
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 51-72 in LWrist_speed_event is completely within zero chunk 0-320 in LWrist_vert_vel_movement_event
Last non-zero chunk 147-165 in LWrist_speed_event is completely within zero chunk 0-320 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 80 101 1333.333333 1683.333333 350.0
3 3 147 165 2450.000000 2750.000000 300.0
column
1 LWrist_speed_event
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_59_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 306 321 5100.0 5350.0 250.0
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 128 130 2133.333333 2166.666667 33.333333
5 6 250 251 4166.666667 4183.333333 16.666667
column
1 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 46 46 766.666667 766.666667 0.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 53 59 883.333333 983.333333 100.000000
3 2 282 289 4700.000000 4816.666667 116.666667
column
1 Head_speed_event
3 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 41 52 683.333333 866.666667 183.333333
7 6 298 305 4966.666667 5083.333333 116.666667
column
1 RElbow_speed_event
7 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 6 286 302 4766.666667 5033.333333 266.666667
column
5 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 38-59 in LWrist_speed_event is completely within zero chunk 0-68 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 266-301 in LWrist_speed_event is completely within zero chunk 248-321 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 41-59 in RWrist_speed_event is completely within zero chunk 0-68 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 68 89 1133.333333 1483.333333 350.0
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_5_p0_annotated.csv
We do not need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 9 9 150.000000 150.0 0.000000
3 2 56 75 933.333333 1250.0 316.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 41 47 683.333333 783.333333 100.0
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 106 123 1766.666667 2050.0 283.333333
column
3 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 12-32 in LWrist_speed_event is completely within zero chunk 0-179 in LWrist_vert_vel_movement_event
Last non-zero chunk 95-128 in LWrist_speed_event is completely within zero chunk 0-179 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 8-73 in RWrist_speed_event partially overlaps with zero chunk 0-28 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 55 65 916.666667 1083.333333 166.666667
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_60_p1_annotated.csv
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 118 130 1966.666667 2166.666667 200.000000
3 2 174 193 2900.000000 3216.666667 316.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 74 91 1233.333333 1516.666667 283.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 133 148 2216.666667 2466.666667 250.0
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 2 0.000000 33.333333 33.333333
2 2 58 77 966.666667 1283.333333 316.666667
column
0 LWrist_vert_vel_movement_event
2 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-1 in LWrist_speed_event is completely within zero chunk 0-205 in LWrist_vert_vel_movement_event
Last non-zero chunk 61-75 in LWrist_speed_event is completely within zero chunk 0-205 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-3 in RWrist_speed_event is completely within zero chunk 0-57 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
value start_idx end_idx start_time end_time duration \
3 3 128 151 2133.333333 2516.666667 383.333333
column
3 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 61 75 1016.666667 1250.0 233.333333
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_61_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 16 21 266.666667 350.000000 83.333333
3 2 81 98 1350.000000 1633.333333 283.333333
5 4 170 183 2833.333333 3050.000000 216.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 52 69 866.666667 1150.0 283.333333
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 3 0.0 50.0 50.0
column
0 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We do not need to merge
value start_idx end_idx start_time end_time duration \
0 1 0 15 0.000000 250.000000 250.000000
2 2 53 74 883.333333 1233.333333 350.000000
4 3 182 183 3033.333333 3050.000000 16.666667
column
0 RWrist_vert_vel_movement_event
2 RWrist_vert_vel_movement_event
4 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 2 13 33.333333 216.666667 183.333333
3 2 52 75 866.666667 1250.000000 383.333333
5 3 171 183 2850.000000 3050.000000 200.000000
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
5 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-15 in LWrist_speed_event is completely within zero chunk 0-183 in LWrist_vert_vel_movement_event
Last non-zero chunk 160-183 in LWrist_speed_event is completely within zero chunk 0-183 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-16 in RWrist_speed_event is completely within zero chunk 0-183 in RWrist_vert_vel_movement_event
Last non-zero chunk 163-183 in RWrist_speed_event is completely within zero chunk 0-183 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
1 2 52 75 866.666667 1250.0 383.333333
column
1 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 120 127 2000.0 2116.666667 116.666667
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_62_p1_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 320 323 5333.333333 5383.333333 50.0
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 15 16 250.0 266.666667 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 311 323 5183.333333 5383.333333 200.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 185 193 3083.333333 3216.666667 133.333333
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 62 75 1033.333333 1250.000000 216.666667
3 2 226 244 3766.666667 4066.666667 300.000000
5 3 311 323 5183.333333 5383.333333 200.000000
column
1 RShoulder_speed_event
3 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 139 144 2316.666667 2400.0 83.333333
column
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 317 323 5283.333333 5383.333333 100.0
column
1 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 323 323 5383.333333 5383.333333 0.0
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 61 69 1016.666667 1150.000000 133.333333
3 2 310 323 5166.666667 5383.333333 216.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 59 64 983.333333 1066.666667 83.333333
3 2 313 323 5216.666667 5383.333333 166.666667
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 57-77 in LWrist_speed_event is completely within zero chunk 0-323 in LWrist_vert_vel_movement_event
Last non-zero chunk 302-323 in LWrist_speed_event is completely within zero chunk 0-323 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_63_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 426 436 7100.0 7266.666667 166.666667
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 140 141 2333.333333 2350.000000 16.666667
3 2 286 287 4766.666667 4783.333333 16.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 232 251 3866.666667 4183.333333 316.666667
5 3 328 333 5466.666667 5550.000000 83.333333
9 5 432 436 7200.000000 7266.666667 66.666667
column
3 LElbow_speed_event
5 LElbow_speed_event
9 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 73 80 1216.666667 1333.333333 116.666667
3 2 186 190 3100.000000 3166.666667 66.666667
column
1 Head_speed_event
3 Head_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 2 154 173 2566.666667 2883.333333 316.666667
column
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 430 436 7166.666667 7266.666667 100.0
column
1 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 422 436 7033.333333 7266.666667 233.333333
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 129 143 2150.000000 2383.333333 233.333333
3 2 191 192 3183.333333 3200.000000 16.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 96 120 1600.0 2000.0 400.0
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 3 0.000000 50.000000 50.000000
4 3 170 174 2833.333333 2900.000000 66.666667
6 4 378 397 6300.000000 6616.666667 316.666667
8 5 431 436 7183.333333 7266.666667 83.333333
column
0 LWrist_vert_vel_movement_event
4 LWrist_vert_vel_movement_event
6 LWrist_vert_vel_movement_event
8 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-5 in LWrist_speed_event is completely within zero chunk 0-98 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 423-436 in LWrist_speed_event is completely within zero chunk 127-436 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-10 in RWrist_speed_event is completely within zero chunk 0-213 in RWrist_vert_vel_movement_event
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 329 337 5483.333333 5616.666667 133.333333
column
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_64_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 9 9 150.000000 150.000000 0.000000
3 2 157 180 2616.666667 3000.000000 383.333333
7 7 369 373 6150.000000 6216.666667 66.666667
11 11 455 461 7583.333333 7683.333333 100.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
7 RHeel_speed_event
11 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 143 160 2383.333333 2666.666667 283.333333
3 3 195 208 3250.000000 3466.666667 216.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 73 74 1216.666667 1233.333333 16.666667
5 4 296 298 4933.333333 4966.666667 33.333333
column
1 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 59 67 983.333333 1116.666667 133.333333
3 2 151 156 2516.666667 2600.000000 83.333333
5 3 218 218 3633.333333 3633.333333 0.000000
7 4 292 295 4866.666667 4916.666667 50.000000
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 58 71 966.666667 1183.333333 216.666667
7 5 432 447 7200.000000 7450.000000 250.000000
column
1 RWrist_vert_vel_movement_event
7 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 48-75 in LWrist_speed_event is completely within zero chunk 0-169 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 427-446 in LWrist_speed_event is completely within zero chunk 238-461 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 51-74 in RWrist_speed_event is completely within zero chunk 0-146 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 424-449 in RWrist_speed_event is completely within zero chunk 414-461 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_65_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 69 69 1150.000000 1150.000000 0.0
3 2 97 100 1616.666667 1666.666667 50.0
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 146 157 2433.333333 2616.666667 183.333333
column
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_67_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 204 205 3400.0 3416.666667 16.666667
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 132 133 2200.0 2216.666667 16.666667
3 2 162 164 2700.0 2733.333333 33.333333
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 89 109 1483.333333 1816.666667 333.333333
3 2 197 205 3283.333333 3416.666667 133.333333
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 4 194 205 3233.333333 3416.666667 183.333333
column
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 51 68 850.000000 1133.333333 283.333333
3 2 190 205 3166.666667 3416.666667 250.000000
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 203 205 3383.333333 3416.666667 33.333333
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 195 205 3250.0 3416.666667 166.666667
column
3 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 42-69 in LWrist_speed_event is completely within zero chunk 0-205 in LWrist_vert_vel_movement_event
Last non-zero chunk 185-205 in LWrist_speed_event is completely within zero chunk 0-205 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 44-73 in RWrist_speed_event is completely within zero chunk 0-205 in RWrist_vert_vel_movement_event
Last non-zero chunk 176-205 in RWrist_speed_event is completely within zero chunk 0-205 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_68_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 62 79 1033.333333 1316.666667 283.333333
3 2 191 204 3183.333333 3400.000000 216.666667
5 3 236 245 3933.333333 4083.333333 150.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 177 198 2950.0 3300.0 350.0
column
1 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 240 242 4000.0 4033.333333 33.333333
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
3 3 295 297 4916.666667 4950.0 33.333333
column
3 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
We do not need to merge
value start_idx end_idx start_time end_time duration \
5 3 273 287 4550.0 4783.333333 233.333333
column
5 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 68 90 1133.333333 1500.0 366.666667
3 2 273 291 4550.000000 4850.0 300.000000
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 46-90 in LWrist_speed_event is completely within zero chunk 0-297 in LWrist_vert_vel_movement_event
Last non-zero chunk 263-297 in LWrist_speed_event is completely within zero chunk 0-297 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 50-70 in RWrist_speed_event is completely within zero chunk 0-164 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 266-296 in RWrist_speed_event is completely within zero chunk 256-297 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_69_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 143 151 2383.333333 2516.666667 133.333333
3 2 186 198 3100.000000 3300.000000 200.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 27 39 450.0 650.000000 200.000000
5 4 327 338 5450.0 5633.333333 183.333333
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 46 50 766.666667 833.333333 66.666667
3 2 106 112 1766.666667 1866.666667 100.000000
5 3 163 170 2716.666667 2833.333333 116.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 326 338 5433.333333 5633.333333 200.0
column
1 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 28 39 466.666667 650.000000 183.333333
5 4 334 338 5566.666667 5633.333333 66.666667
column
1 LShoulder_speed_event
5 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
We do not need to merge
value start_idx end_idx start_time end_time duration \
3 2 161 173 2683.333333 2883.333333 200.0
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 315 329 5250.0 5483.333333 233.333333
column
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 31-54 in LWrist_speed_event is completely within zero chunk 0-103 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 311-338 in LWrist_speed_event is completely within zero chunk 131-338 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 32-55 in RWrist_speed_event is completely within zero chunk 0-87 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 284-338 in RWrist_speed_event partially overlaps with zero chunk 331-338 in RWrist_vert_vel_movement_event
We do not need to merge
value start_idx end_idx start_time end_time duration \
3 3 164 166 2733.333333 2766.666667 33.333333
column
3 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 106 128 1766.666667 2133.333333 366.666667
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_6_p0_annotated.csv
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
0 1 0 4 0.0 66.666667 66.666667
column
0 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 130 134 2166.666667 2233.333333 66.666667
3 2 279 291 4650.000000 4850.000000 200.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 16 0.000000 266.666667 266.666667
2 2 62 64 1033.333333 1066.666667 33.333333
4 3 126 130 2100.000000 2166.666667 66.666667
6 4 271 286 4516.666667 4766.666667 250.000000
column
0 RShoulder_speed_event
2 RShoulder_speed_event
4 RShoulder_speed_event
6 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 8 19 133.333333 316.666667 183.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 12 0.0 200.000000 200.000000
2 2 279 290 4650.0 4833.333333 183.333333
column
0 LShoulder_speed_event
2 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
3 4 163 178 2716.666667 2966.666667 250.0
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 3-25 in LWrist_speed_event is completely within zero chunk 0-318 in LWrist_vert_vel_movement_event
Last non-zero chunk 3-25 in LWrist_speed_event is completely within zero chunk 0-318 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 8-24 in RWrist_speed_event is completely within zero chunk 0-52 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 247-288 in RWrist_speed_event partially overlaps with zero chunk 280-318 in RWrist_vert_vel_movement_event
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_70_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 269 282 4483.333333 4700.000000 216.666667
3 2 301 316 5016.666667 5266.666667 250.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 38-53 in LWrist_speed_event is completely within zero chunk 0-455 in LWrist_vert_vel_movement_event
Last non-zero chunk 426-436 in LWrist_speed_event is completely within zero chunk 0-455 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 42-57 in RWrist_speed_event is completely within zero chunk 0-455 in RWrist_vert_vel_movement_event
Last non-zero chunk 426-439 in RWrist_speed_event is completely within zero chunk 0-455 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
1 2 262 268 4366.666667 4466.666667 100.000000
3 3 288 296 4800.000000 4933.333333 133.333333
column
1 RWrist_speed_event
3 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 282 292 4700.000000 4866.666667 166.666667
3 3 326 327 5433.333333 5450.000000 16.666667
column
1 LWrist_speed_event
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_71_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 54-60 in LWrist_speed_event is completely within zero chunk 0-246 in LWrist_vert_vel_movement_event
Last non-zero chunk 218-226 in LWrist_speed_event is completely within zero chunk 0-246 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 52-64 in RWrist_speed_event is completely within zero chunk 0-246 in RWrist_vert_vel_movement_event
Last non-zero chunk 216-235 in RWrist_speed_event is completely within zero chunk 0-246 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_72_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 255 256 4250.0 4266.666667 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 251 259 4183.333333 4316.666667 133.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 68 73 1133.333333 1216.666667 83.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 247 259 4116.666667 4316.666667 200.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 47-66 in LWrist_speed_event is completely within zero chunk 0-301 in LWrist_vert_vel_movement_event
Last non-zero chunk 240-255 in LWrist_speed_event is completely within zero chunk 0-301 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 48-69 in RWrist_speed_event is completely within zero chunk 0-301 in RWrist_vert_vel_movement_event
Last non-zero chunk 240-260 in RWrist_speed_event is completely within zero chunk 0-301 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_73_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 239 240 3983.333333 4000.0 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 39 48 650.0 800.000000 150.000000
3 2 258 262 4300.0 4366.666667 66.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 40 45 666.666667 750.0 83.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 40-63 in LWrist_speed_event is completely within zero chunk 0-320 in LWrist_vert_vel_movement_event
Last non-zero chunk 239-251 in LWrist_speed_event is completely within zero chunk 0-320 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 40-66 in RWrist_speed_event is completely within zero chunk 0-320 in RWrist_vert_vel_movement_event
Last non-zero chunk 239-252 in RWrist_speed_event is completely within zero chunk 0-320 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
1 2 239 252 3983.333333 4200.0 216.666667
column
1 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 239 251 3983.333333 4183.333333 200.0
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_74_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 296 302 4933.333333 5033.333333 100.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 282 288 4700.0 4800.0 100.0
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 298 302 4966.666667 5033.333333 66.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 40-55 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event
Last non-zero chunk 271-282 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-61 in RWrist_speed_event is completely within zero chunk 0-302 in RWrist_vert_vel_movement_event
Last non-zero chunk 276-302 in RWrist_speed_event is completely within zero chunk 0-302 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_75_p0_annotated.csv
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 301 305 5016.666667 5083.333333 66.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 36 36 600.0 600.0 0.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 54 68 900.0 1133.333333 233.333333
3 2 282 295 4700.0 4916.666667 216.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 52 71 866.666667 1183.333333 316.666667
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 35-70 in LWrist_speed_event is completely within zero chunk 0-270 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 271-308 in LWrist_speed_event partially overlaps with zero chunk 299-320 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 34-67 in RWrist_speed_event is completely within zero chunk 0-320 in RWrist_vert_vel_movement_event
Last non-zero chunk 271-305 in RWrist_speed_event is completely within zero chunk 0-320 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_76_p0_annotated.csv
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 30 41 500.000000 683.333333 183.333333
3 2 187 203 3116.666667 3383.333333 266.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 36 48 600.0 800.000000 200.000000
3 2 186 197 3100.0 3283.333333 183.333333
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 143 154 2383.333333 2566.666667 183.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 33-53 in LWrist_speed_event is completely within zero chunk 0-224 in LWrist_vert_vel_movement_event
Last non-zero chunk 179-196 in LWrist_speed_event is completely within zero chunk 0-224 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-56 in RWrist_speed_event is completely within zero chunk 0-224 in RWrist_vert_vel_movement_event
Last non-zero chunk 182-202 in RWrist_speed_event is completely within zero chunk 0-224 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_77_p0_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 242 253 4033.333333 4216.666667 183.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 38 44 633.333333 733.333333 100.0
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 245 252 4083.333333 4200.0 116.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 24-39 in LWrist_speed_event is completely within zero chunk 0-272 in LWrist_vert_vel_movement_event
Last non-zero chunk 225-243 in LWrist_speed_event is completely within zero chunk 0-272 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 25-46 in RWrist_speed_event is completely within zero chunk 0-272 in RWrist_vert_vel_movement_event
Last non-zero chunk 235-250 in RWrist_speed_event is completely within zero chunk 0-272 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_78_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 192 197 3200.000000 3283.333333 83.333333
3 2 238 240 3966.666667 4000.000000 33.333333
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 42 52 700.000000 866.666667 166.666667
3 2 205 207 3416.666667 3450.000000 33.333333
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 30-39 in LWrist_speed_event is completely within zero chunk 0-257 in LWrist_vert_vel_movement_event
Last non-zero chunk 223-239 in LWrist_speed_event is completely within zero chunk 0-257 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 26-42 in RWrist_speed_event is completely within zero chunk 0-257 in RWrist_vert_vel_movement_event
Last non-zero chunk 205-231 in RWrist_speed_event is completely within zero chunk 0-257 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_79_p0_annotated.csv
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 167 170 2783.333333 2833.333333 50.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 27 32 450.0 533.333333 83.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 79 97 1316.666667 1616.666667 300.000000
5 4 210 220 3500.000000 3666.666667 166.666667
column
3 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 34-43 in LWrist_speed_event is completely within zero chunk 0-254 in LWrist_vert_vel_movement_event
Last non-zero chunk 34-43 in LWrist_speed_event is completely within zero chunk 0-254 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 27-55 in RWrist_speed_event partially overlaps with zero chunk 0-28 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 181-225 in RWrist_speed_event is completely within zero chunk 101-254 in RWrist_vert_vel_movement_event
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_7_p0_annotated.csv
We need to merge
We do not need to merge
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 374 374 6233.333333 6233.333333 0.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 335 353 5583.333333 5883.333333 300.000000
5 4 373 392 6216.666667 6533.333333 316.666667
column
3 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 20 38 333.333333 633.333333 300.000000
3 2 179 186 2983.333333 3100.000000 116.666667
5 3 399 402 6650.000000 6700.000000 50.000000
7 4 441 446 7350.000000 7433.333333 83.333333
column
1 RShoulder_speed_event
3 RShoulder_speed_event
5 RShoulder_speed_event
7 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
7 5 441 449 7350.0 7483.333333 133.333333
column
7 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 21 34 350.0 566.666667 216.666667
3 2 114 131 1900.0 2183.333333 283.333333
5 3 174 183 2900.0 3050.000000 150.000000
7 4 438 445 7300.0 7416.666667 116.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
value start_idx end_idx start_time end_time duration \
3 2 181 190 3016.666667 3166.666667 150.0
column
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-36 in LWrist_speed_event is completely within zero chunk 0-102 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 431-446 in LWrist_speed_event is completely within zero chunk 397-472 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 23-39 in RWrist_speed_event is completely within zero chunk 0-105 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 429-448 in RWrist_speed_event is completely within zero chunk 398-472 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
3 5 244 255 4066.666667 4250.0 183.333333
column
3 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_80_p0_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 21 31 350.0 516.666667 166.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 37 45 616.666667 750.0 133.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 22 29 366.666667 483.333333 116.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 24-43 in LWrist_speed_event is completely within zero chunk 0-204 in LWrist_vert_vel_movement_event
Last non-zero chunk 165-170 in LWrist_speed_event is completely within zero chunk 0-204 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 22-47 in RWrist_speed_event is completely within zero chunk 0-204 in RWrist_vert_vel_movement_event
Last non-zero chunk 163-176 in RWrist_speed_event is completely within zero chunk 0-204 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_81_p0_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 20 36 333.333333 600.000000 266.666667
3 2 248 248 4133.333333 4133.333333 0.000000
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 75 86 1250.0 1433.333333 183.333333
column
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 21 35 350.000000 583.333333 233.333333
3 2 178 191 2966.666667 3183.333333 216.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 29 50 483.333333 833.333333 350.0
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 24-42 in LWrist_speed_event is completely within zero chunk 0-248 in LWrist_vert_vel_movement_event
Last non-zero chunk 24-42 in LWrist_speed_event is completely within zero chunk 0-248 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 23-55 in RWrist_speed_event is completely within zero chunk 0-70 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 242-248 in RWrist_speed_event is completely within zero chunk 97-248 in RWrist_vert_vel_movement_event
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 2 76 91 1266.666667 1516.666667 250.000000
3 3 178 194 2966.666667 3233.333333 266.666667
column
1 RWrist_speed_event
3 RWrist_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_82_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 74 86 1233.333333 1433.333333 200.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 72 87 1200.0 1450.0 250.0
3 2 147 165 2450.0 2750.0 300.0
5 3 186 195 3100.0 3250.0 150.0
column
1 RElbow_speed_event
3 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 79 84 1316.666667 1400.000000 83.333333
5 4 173 185 2883.333333 3083.333333 200.000000
column
1 RWrist_vert_vel_movement_event
5 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 35-51 in LWrist_speed_event is completely within zero chunk 0-317 in LWrist_vert_vel_movement_event
Last non-zero chunk 230-246 in LWrist_speed_event is completely within zero chunk 0-317 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 34-51 in RWrist_speed_event is completely within zero chunk 0-110 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 301-317 in RWrist_speed_event is completely within zero chunk 244-317 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 75 78 1250.000000 1300.000000 50.0
3 3 115 118 1916.666667 1966.666667 50.0
column
1 LWrist_speed_event
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_83_p0_annotated.csv
We need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 224 241 3733.333333 4016.666667 283.333333
3 3 285 290 4750.000000 4833.333333 83.333333
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 36 37 600.0 616.666667 16.666667
3 2 69 75 1150.0 1250.000000 100.000000
5 3 294 300 4900.0 5000.000000 100.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 206 209 3433.333333 3483.333333 50.0
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 19 29 316.666667 483.333333 166.666667
3 2 223 234 3716.666667 3900.000000 183.333333
5 3 294 308 4900.000000 5133.333333 233.333333
column
1 RShoulder_speed_event
3 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 223 231 3716.666667 3850.000000 133.333333
3 2 289 302 4816.666667 5033.333333 216.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 16 29 266.666667 483.333333 216.666667
3 2 296 308 4933.333333 5133.333333 200.000000
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 22-41 in LWrist_speed_event is completely within zero chunk 0-333 in LWrist_vert_vel_movement_event
Last non-zero chunk 267-306 in LWrist_speed_event is completely within zero chunk 0-333 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 22-52 in RWrist_speed_event is completely within zero chunk 0-172 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 136-309 in RWrist_speed_event partially overlaps with zero chunk 272-333 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
value start_idx end_idx start_time end_time duration \
1 2 62 69 1033.333333 1150.0 116.666667
column
1 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 67 69 1116.666667 1150.0 33.333333
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_84_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 137 137 2283.333333 2283.333333 0.0
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 258 274 4300.0 4566.666667 266.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 181 197 3016.666667 3283.333333 266.666667
3 2 251 258 4183.333333 4300.000000 116.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 200 202 3333.333333 3366.666667 33.333333
3 2 257 273 4283.333333 4550.000000 266.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 18-35 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event
Last non-zero chunk 18-35 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 16-35 in RWrist_speed_event is completely within zero chunk 0-131 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 128-272 in RWrist_speed_event partially overlaps with zero chunk 229-302 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_85_p0_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 231 241 3850.0 4016.666667 166.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 230 243 3833.333333 4050.0 216.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 233-249 in LWrist_speed_event is completely within zero chunk 0-249 in LWrist_vert_vel_movement_event
Last non-zero chunk 233-249 in LWrist_speed_event is completely within zero chunk 0-249 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 235-249 in RWrist_speed_event is completely within zero chunk 0-249 in RWrist_vert_vel_movement_event
Last non-zero chunk 235-249 in RWrist_speed_event is completely within zero chunk 0-249 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_86_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 118 119 1966.666667 1983.333333 16.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 176 186 2933.333333 3100.0 166.666667
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 53-61 in LWrist_speed_event is completely within zero chunk 0-221 in LWrist_vert_vel_movement_event
Last non-zero chunk 165-182 in LWrist_speed_event is completely within zero chunk 0-221 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 51-65 in RWrist_speed_event is completely within zero chunk 0-221 in RWrist_vert_vel_movement_event
Last non-zero chunk 168-184 in RWrist_speed_event is completely within zero chunk 0-221 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 165 182 2750.0 3033.333333 283.333333
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_87_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 39 50 650.0 833.333333 183.333333
3 2 180 189 3000.0 3150.000000 150.000000
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 32-45 in LWrist_speed_event is completely within zero chunk 0-221 in LWrist_vert_vel_movement_event
Last non-zero chunk 173-178 in LWrist_speed_event is completely within zero chunk 0-221 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-46 in RWrist_speed_event is completely within zero chunk 0-221 in RWrist_vert_vel_movement_event
Last non-zero chunk 170-191 in RWrist_speed_event is completely within zero chunk 0-221 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_88_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 129 143 2150.0 2383.333333 233.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 34 42 566.666667 700.0 133.333333
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 128 139 2133.333333 2316.666667 183.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 26-44 in LWrist_speed_event is completely within zero chunk 0-176 in LWrist_vert_vel_movement_event
Last non-zero chunk 121-132 in LWrist_speed_event is completely within zero chunk 0-176 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 28-50 in RWrist_speed_event is completely within zero chunk 0-176 in RWrist_vert_vel_movement_event
Last non-zero chunk 127-135 in RWrist_speed_event is completely within zero chunk 0-176 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
1 2 127 135 2116.666667 2250.0 133.333333
column
1 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 121 132 2016.666667 2200.0 183.333333
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_89_p0_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 2 8 33.333333 133.333333 100.0
3 2 230 230 3833.333333 3833.333333 0.0
column
1 RHeel_speed_event
3 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 35 46 583.333333 766.666667 183.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 305 314 5083.333333 5233.333333 150.0
column
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 210 219 3500.0 3650.000000 150.000000
3 2 261 263 4350.0 4383.333333 33.333333
column
1 LHip_speed_event
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 35 46 583.333333 766.666667 183.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 36-41 in LWrist_speed_event is completely within zero chunk 0-333 in LWrist_vert_vel_movement_event
Last non-zero chunk 282-294 in LWrist_speed_event is completely within zero chunk 0-333 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 34-50 in RWrist_speed_event is completely within zero chunk 0-184 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 298-313 in RWrist_speed_event is completely within zero chunk 253-333 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 57 57 950.0 950.0 0.0
column
1 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_8_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 153 166 2550.0 2766.666667 216.666667
column
1 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 91 109 1516.666667 1816.666667 300.0
column
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
0 1 0 3 0.0 50.0 50.0
column
0 RKnee_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
0 1 0 10 0.0 166.666667 166.666667
column
0 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-14 in LWrist_speed_event is completely within zero chunk 0-189 in LWrist_vert_vel_movement_event
Last non-zero chunk 152-162 in LWrist_speed_event is completely within zero chunk 0-189 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 152-167 in RWrist_speed_event is completely within zero chunk 137-189 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_90_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration column
1 1 117 123 1950.0 2050.0 100.0 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 39 50 650.0 833.333333 183.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 117 119 1950.0 1983.333333 33.333333
column
1 LHip_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 125 127 2083.333333 2116.666667 33.333333
column
1 RKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 56-67 in LWrist_speed_event is completely within zero chunk 0-234 in LWrist_vert_vel_movement_event
Last non-zero chunk 56-67 in LWrist_speed_event is completely within zero chunk 0-234 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 37-156 in RWrist_speed_event partially overlaps with zero chunk 0-39 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 164-187 in RWrist_speed_event is completely within zero chunk 141-234 in RWrist_vert_vel_movement_event
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_91_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 9 25 150.0 416.666667 266.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 117 118 1950.0 1966.666667 16.666667
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 173 179 2883.333333 2983.333333 100.0
column
3 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 31 51 516.666667 850.000000 333.333333
3 3 101 120 1683.333333 2000.000000 316.666667
5 4 170 178 2833.333333 2966.666667 133.333333
column
1 LShoulder_speed_event
3 LShoulder_speed_event
5 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 27-42 in LWrist_speed_event is completely within zero chunk 0-188 in LWrist_vert_vel_movement_event
Last non-zero chunk 152-164 in LWrist_speed_event is completely within zero chunk 0-188 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-43 in RWrist_speed_event is completely within zero chunk 0-48 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 160-171 in RWrist_speed_event is completely within zero chunk 139-188 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_92_p1_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 248 255 4133.333333 4250.0 116.666667
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 157 165 2616.666667 2750.000000 133.333333
3 3 204 214 3400.000000 3566.666667 166.666667
5 5 248 248 4133.333333 4133.333333 0.000000
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 235 255 3916.666667 4250.0 333.333333
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 234 255 3900.0 4250.0 350.0
column
1 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 238 255 3966.666667 4250.0 283.333333
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 233-255 in LWrist_speed_event is completely within zero chunk 0-255 in LWrist_vert_vel_movement_event
Last non-zero chunk 233-255 in LWrist_speed_event is completely within zero chunk 0-255 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 228-255 in RWrist_speed_event is completely within zero chunk 0-255 in RWrist_vert_vel_movement_event
Last non-zero chunk 228-255 in RWrist_speed_event is completely within zero chunk 0-255 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_93_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 105 105 1750.000000 1750.000000 0.000000
3 2 161 178 2683.333333 2966.666667 283.333333
5 4 246 250 4100.000000 4166.666667 66.666667
7 5 293 294 4883.333333 4900.000000 16.666667
column
1 RHeel_speed_event
3 RHeel_speed_event
5 RHeel_speed_event
7 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 2 202 217 3366.666667 3616.666667 250.000000
5 3 288 293 4800.000000 4883.333333 83.333333
column
3 LElbow_speed_event
5 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 78 83 1300.000000 1383.333333 83.333333
5 5 260 265 4333.333333 4416.666667 83.333333
column
1 Head_speed_event
5 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 41 56 683.333333 933.333333 250.0
5 4 288 306 4800.000000 5100.000000 300.0
column
1 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 51 59 850.000000 983.333333 133.333333
5 5 286 298 4766.666667 4966.666667 200.000000
column
1 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
3 3 293 297 4883.333333 4950.0 66.666667
column
3 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 40 56 666.666667 933.333333 266.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
We do not need to merge
value start_idx end_idx start_time end_time duration \
1 1 44 56 733.333333 933.333333 200.000000
3 2 285 302 4750.000000 5033.333333 283.333333
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 295 298 4916.666667 4966.666667 50.0
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 36-61 in LWrist_speed_event is completely within zero chunk 0-312 in LWrist_vert_vel_movement_event
Last non-zero chunk 276-304 in LWrist_speed_event is completely within zero chunk 0-312 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 35-64 in RWrist_speed_event is completely within zero chunk 0-312 in RWrist_vert_vel_movement_event
Last non-zero chunk 279-305 in RWrist_speed_event is completely within zero chunk 0-312 in RWrist_vert_vel_movement_event
value start_idx end_idx start_time end_time duration \
1 2 155 159 2583.333333 2650.0 66.666667
3 3 209 210 3483.333333 3500.0 16.666667
column
1 RWrist_speed_event
3 RWrist_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 2 167 176 2783.333333 2933.333333 150.000000
3 3 199 215 3316.666667 3583.333333 266.666667
column
1 LWrist_speed_event
3 LWrist_speed_event
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_94_p1_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 55 69 916.666667 1150.0 233.333333
3 2 195 198 3250.000000 3300.0 50.000000
column
1 LElbow_speed_event
3 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 238 244 3966.666667 4066.666667 100.0
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 53 55 883.333333 916.666667 33.333333
3 2 140 160 2333.333333 2666.666667 333.333333
7 4 266 267 4433.333333 4450.000000 16.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
7 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 58 77 966.666667 1283.333333 316.666667
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 51 53 850.0 883.333333 33.333333
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We need to merge
value start_idx end_idx start_time end_time duration \
1 1 47 60 783.333333 1000.000000 216.666667
3 2 183 197 3050.000000 3283.333333 233.333333
5 3 260 267 4333.333333 4450.000000 116.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
5 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 40-66 in LWrist_speed_event is completely within zero chunk 0-184 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 254-267 in LWrist_speed_event is completely within zero chunk 221-267 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 43-72 in RWrist_speed_event is completely within zero chunk 0-267 in RWrist_vert_vel_movement_event
Last non-zero chunk 256-267 in RWrist_speed_event is completely within zero chunk 0-267 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_95_p1_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 129 143 2150.000000 2383.333333 233.333333
5 6 295 296 4916.666667 4933.333333 16.666667
column
1 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 144 148 2400.0 2466.666667 66.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 65 68 1083.333333 1133.333333 50.0
column
1 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
5 3 189 203 3150.000000 3383.333333 233.333333
9 6 355 359 5916.666667 5983.333333 66.666667
column
5 RShoulder_speed_event
9 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 65 71 1083.333333 1183.333333 100.000000
3 2 206 209 3433.333333 3483.333333 50.000000
5 3 300 310 5000.000000 5166.666667 166.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 62 69 1033.333333 1150.0 116.666667
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 58 76 966.666667 1266.666667 300.000000
5 3 194 214 3233.333333 3566.666667 333.333333
7 4 301 321 5016.666667 5350.000000 333.333333
column
1 LShoulder_speed_event
5 LShoulder_speed_event
7 LShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 58 70 966.666667 1166.666667 200.000000
3 2 298 314 4966.666667 5233.333333 266.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 307 313 5116.666667 5216.666667 100.0
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 50-74 in LWrist_speed_event is completely within zero chunk 0-359 in LWrist_vert_vel_movement_event
Last non-zero chunk 291-317 in LWrist_speed_event is completely within zero chunk 0-359 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 55-80 in RWrist_speed_event is completely within zero chunk 0-359 in RWrist_vert_vel_movement_event
Last non-zero chunk 291-318 in RWrist_speed_event is completely within zero chunk 0-359 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_96_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We do not need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 78 94 1300.0 1566.666667 266.666667
column
1 RHeel_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 54 64 900.000000 1066.666667 166.666667
3 2 184 195 3066.666667 3250.000000 183.333333
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 57 69 950.000000 1150.000000 200.000000
3 2 284 300 4733.333333 5000.000000 266.666667
5 3 330 346 5500.000000 5766.666667 266.666667
column
1 RElbow_speed_event
3 RElbow_speed_event
5 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 297 320 4950.0 5333.333333 383.333333
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 54 63 900.000000 1050.0 150.000000
3 2 206 219 3433.333333 3650.0 216.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 52 62 866.666667 1033.333333 166.666667
3 2 317 334 5283.333333 5566.666667 283.333333
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 44-67 in LWrist_speed_event is completely within zero chunk 0-360 in LWrist_vert_vel_movement_event
Last non-zero chunk 295-335 in LWrist_speed_event is completely within zero chunk 0-360 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 45-72 in RWrist_speed_event is completely within zero chunk 0-360 in RWrist_vert_vel_movement_event
Last non-zero chunk 312-343 in RWrist_speed_event is completely within zero chunk 0-360 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_97_p1_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 382 391 6366.666667 6516.666667 150.0
column
1 LKnee_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 11 11 183.333333 183.333333 0.0
5 7 373 376 6216.666667 6266.666667 50.0
column
1 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 381 391 6350.0 6516.666667 166.666667
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 373 391 6216.666667 6516.666667 300.0
column
1 RShoulder_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 211 213 3516.666667 3550.0 33.333333
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 192 195 3200.000000 3250.000000 50.000000
3 2 374 391 6233.333333 6516.666667 283.333333
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
value start_idx end_idx start_time end_time duration \
1 1 366 381 6100.0 6350.0 250.0
column
1 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 365 368 6083.333333 6133.333333 50.0
column
1 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 362-391 in LWrist_speed_event is completely within zero chunk 0-391 in LWrist_vert_vel_movement_event
Last non-zero chunk 362-391 in LWrist_speed_event is completely within zero chunk 0-391 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 366-391 in RWrist_speed_event is completely within zero chunk 0-391 in RWrist_vert_vel_movement_event
Last non-zero chunk 366-391 in RWrist_speed_event is completely within zero chunk 0-391 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_98_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 297 303 4950.0 5050.0 100.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
7 5 322 323 5366.666667 5383.333333 16.666667
column
7 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 56 66 933.333333 1100.000000 166.666667
3 2 107 119 1783.333333 1983.333333 200.000000
5 3 201 214 3350.000000 3566.666667 216.666667
column
1 RShoulder_speed_event
3 RShoulder_speed_event
5 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 293 305 4883.333333 5083.333333 200.0
column
1 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 56 66 933.333333 1100.0 166.666667
column
1 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
value start_idx end_idx start_time end_time duration \
1 1 52 61 866.666667 1016.666667 150.000000
3 2 293 309 4883.333333 5150.000000 266.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 46-66 in LWrist_speed_event is completely within zero chunk 0-323 in LWrist_vert_vel_movement_event
Last non-zero chunk 291-312 in LWrist_speed_event is completely within zero chunk 0-323 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 48-71 in RWrist_speed_event is completely within zero chunk 0-323 in RWrist_vert_vel_movement_event
Last non-zero chunk 287-311 in RWrist_speed_event is completely within zero chunk 0-323 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_99_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 99 116 1650.000000 1933.333333 283.333333
5 6 316 321 5266.666667 5350.000000 83.333333
column
1 RHeel_speed_event
5 RHeel_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 134 143 2233.333333 2383.333333 150.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
5 5 367 371 6116.666667 6183.333333 66.666667
column
5 Head_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 33 47 550.000000 783.333333 233.333333
5 3 220 239 3666.666667 3983.333333 316.666667
7 4 338 354 5633.333333 5900.000000 266.666667
column
1 RShoulder_speed_event
5 RShoulder_speed_event
7 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 46 56 766.666667 933.333333 166.666667
3 2 116 131 1933.333333 2183.333333 250.000000
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 39 41 650.0 683.333333 33.333333
column
1 LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 32 47 533.333333 783.333333 250.0
5 5 337 355 5616.666667 5916.666667 300.0
column
1 LShoulder_speed_event
5 LShoulder_speed_event
We need to turn fake events into 0s
We do not need to merge
We do not need to merge
value start_idx end_idx start_time end_time duration \
1 1 32 44 533.333333 733.333333 200.000000
3 2 344 357 5733.333333 5950.000000 216.666667
column
1 RWrist_vert_vel_movement_event
3 RWrist_vert_vel_movement_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 25 30 416.666667 500.0 83.333333
3 2 355 357 5916.666667 5950.0 33.333333
column
1 LWrist_vert_vel_movement_event
3 LWrist_vert_vel_movement_event
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 22-49 in LWrist_speed_event is completely within zero chunk 0-371 in LWrist_vert_vel_movement_event
Last non-zero chunk 340-360 in LWrist_speed_event is completely within zero chunk 0-371 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 29-52 in RWrist_speed_event is completely within zero chunk 0-371 in RWrist_vert_vel_movement_event
Last non-zero chunk 338-359 in RWrist_speed_event is completely within zero chunk 0-371 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_9_p0_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 188 188 3133.333333 3133.333333 0.0
column
1 LElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 15 18 250.0 300.000000 50.000000
3 2 180 194 3000.0 3233.333333 233.333333
column
1 RShoulder_speed_event
3 RShoulder_speed_event
We need to turn fake events into 0s
value start_idx end_idx start_time end_time duration \
1 1 17 21 283.333333 350.0 66.666667
3 2 184 192 3066.666667 3200.0 133.333333
column
1 RElbow_speed_event
3 RElbow_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 1 14 23 233.333333 383.333333 150.000000
3 2 187 194 3116.666667 3233.333333 116.666667
column
1 LShoulder_speed_event
3 LShoulder_speed_event
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 11-27 in LWrist_speed_event is completely within zero chunk 0-232 in LWrist_vert_vel_movement_event
Last non-zero chunk 172-190 in LWrist_speed_event is completely within zero chunk 0-232 in LWrist_vert_vel_movement_event
Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 15-27 in RWrist_speed_event is completely within zero chunk 0-232 in RWrist_vert_vel_movement_event
Last non-zero chunk 177-195 in RWrist_speed_event is completely within zero chunk 0-232 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
value start_idx end_idx start_time end_time duration \
1 2 172 190 2866.666667 3166.666667 300.0
column
1 LWrist_speed_event
We need to turn fake events into 0s
Merge them into tiers¶
In [331]:
elanfiles = glob.glob(annofolder + '/*ELAN_anno.csv')
# group mapping
group_mapping = {
'Head_speed_event': 'head_mov',
'RShoulder_speed_event': 'upper_body',
'LShoulder_speed_event': 'upper_body',
'RWrist_speed_event': 'arms',
'LWrist_speed_event': 'arms',
'RElbow_speed_event': 'arms',
'LElbow_speed_event': 'arms',
'RHip_speed_event': 'lower_body',
'LHip_speed_event': 'lower_body',
'RKnee_speed_event': 'lower_body',
'LKnee_speed_event': 'lower_body',
'RAnkle_speed_event': 'lower_body',
'LAnkle_speed_event': 'lower_body',
'RHeel_speed_event': 'lower_body',
'LHeel_speed_event': 'lower_body'}
# skipping elbows for now
# Extract unique groups
groups = set(group_mapping.values())
# for each file, annotate the groups
for file in elanfiles:
print('working on ' + file)
# load the file
df = pd.read_csv(file)
# Initialize new columns for each group with 'nomovement'
for group in groups:
df[group] = 'nomovement'
# Iterate over each row and update the group columns
for index, row in df.iterrows():
for keypoint, status in row.items():
if keypoint in group_mapping and status == 'movement':
group = group_mapping[keypoint]
df.at[index, group] = 'movement'
# create column 'movement_in_trial' that checks first row of arms, lower_body, upper_body, head_mov where there is a movement in any of those and last one, and everything in between is movement, elsewhere it is nomovement
df['movement_in_trial'] = 'nomovement'
# check what is the first row in arms, lower_body, upper_body, head_mov where there is movement
# first check whether there is any value 'movement' in any of the columns or it's only 'nomovement', if yes, get the index of the first one
if 'movement' in df['arms'].tolist() or 'movement' in df['lower_body'].tolist() or 'movement' in df['upper_body'].tolist():
first_movement = df[(df['arms'] == 'movement') | (df['lower_body'] == 'movement') | (df['upper_body'] == 'movement')].index[0] #| (df['head_mov'] == 'movement')].index[0]
last_movement = df[(df['arms'] == 'movement') | (df['lower_body'] == 'movement') | (df['upper_body'] == 'movement')].index[-1] #| (df['head_mov'] == 'movement')].index[-1]
# everything in between is movement
df.loc[first_movement:last_movement, 'movement_in_trial'] = 'movement'
else:
print('No movement in this trial')
# in arms check first row of movement and last and everything in between is a movement ## FLAGG maybe this will neeed to be later optimized if there is a big gap between movements
if 'movement' in df['arms'].tolist():
first_arm = df[df['arms'] == 'movement'].index[0]
last_arm = df[df['arms'] == 'movement'].index[-1]
df.loc[first_arm:last_arm, 'arms'] = 'movement'
#head
if 'movement' in df['head_mov'].tolist():
first_head = df[df['head_mov'] == 'movement'].index[0]
last_head = df[df['head_mov'] == 'movement'].index[-1]
df.loc[first_head:last_head, 'head_mov'] = 'movement'
# upper
if 'movement' in df['upper_body'].tolist():
first_upper = df[df['upper_body'] == 'movement'].index[0]
last_upper = df[df['upper_body'] == 'movement'].index[-1]
df.loc[first_upper:last_upper, 'upper_body'] = 'movement'
# lower
if 'movement' in df['lower_body'].tolist():
first_lower = df[df['lower_body'] == 'movement'].index[0]
last_lower = df[df['lower_body'] == 'movement'].index[-1]
df.loc[first_lower:last_lower, 'lower_body'] = 'movement'
# get rid of all event columns
df = df[[x for x in df.columns if 'event' not in x]]
# save the annotated file
df.to_csv(file.replace('ELAN_anno', 'ELAN_tiers'), index=False)
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_0_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_10_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_11_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_12_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_13_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_14_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_15_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_16_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_17_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_18_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_19_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_1_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_20_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_21_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_22_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_23_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_24_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_25_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_26_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_27_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_28_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_29_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_2_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_30_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_31_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_32_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_33_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_35_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_36_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_37_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_38_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_39_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_3_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_40_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_41_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_42_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_43_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_44_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_45_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_46_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_47_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_48_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_49_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_4_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_50_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_51_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_52_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_53_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_5_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_6_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_7_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_8_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_9_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_tpose_0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_tpose_1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_0_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_100_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_101_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_102_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_103_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_104_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_105_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_106_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_107_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_108_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_109_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_10_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_110_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_111_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_112_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_113_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_11_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_12_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_13_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_14_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_15_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_16_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_17_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_18_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_19_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_1_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_20_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_21_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_22_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_23_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_24_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_25_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_26_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_27_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_28_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_29_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_2_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_30_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_31_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_32_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_33_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_34_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_35_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_36_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_37_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_38_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_39_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_3_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_40_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_41_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_43_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_44_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_45_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_46_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_47_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_48_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_49_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_4_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_50_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_51_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_52_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_53_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_54_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_55_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_56_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_57_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_58_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_59_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_5_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_60_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_61_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_62_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_63_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_64_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_65_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_67_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_68_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_69_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_6_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_70_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_71_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_72_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_73_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_74_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_75_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_76_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_77_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_78_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_79_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_7_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_80_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_81_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_82_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_83_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_84_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_85_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_86_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_87_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_88_p0_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_89_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_8_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_90_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_91_p0_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_92_p1_ELAN_anno.csv No movement in this trial working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_93_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_94_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_95_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_96_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_97_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_98_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_99_p1_ELAN_anno.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_9_p0_ELAN_anno.csv No movement in this trial
Create ELAN files¶
function¶
In [27]:
from xml.etree import ElementTree as ET
# Function to create ELAN file
def create_eaf(chunks_dict, output_file):
annotation_document = ET.Element('ANNOTATION_DOCUMENT', {
'xmlns:xsi': "http://www.w3.org/2001/XMLSchema-instance",
'AUTHOR': "",
'DATE': "2024-05-28T11:54:22+01:00",
'FORMAT': "3.0",
'VERSION': "3.0",
'xsi:noNamespaceSchemaLocation': "http://www.mpi.nl/tools/elan/EAFv3.0.xsd"
})
header = ET.SubElement(annotation_document, 'HEADER', {'MEDIA_FILE': "", 'TIME_UNITS': "milliseconds"})
ET.SubElement(header, 'MEDIA_DESCRIPTOR', {
'MEDIA_URL': "file:///path_to_your_file.wav",
'MIME_TYPE': "audio/x-wav"
})
ET.SubElement(header, 'MEDIA_DESCRIPTOR', {
'MEDIA_URL': "file:///path_to_your_file.avi",
'MIME_TYPE': "video/*",
'RELATIVE_MEDIA_URL': "./path_to_your_file.avi"
})
ET.SubElement(header, 'PROPERTY', {'NAME': "URN"}).text = "urn:nl-mpi-tools-elan-eaf:73467978-4930-486d-a56b-fa6acb05e357"
ET.SubElement(header, 'PROPERTY', {'NAME': "lastUsedAnnotationId"}).text = "3"
time_order = ET.SubElement(annotation_document, 'TIME_ORDER')
ts_id_counter = 1
time_slot_map = {}
# Generate time slots for all chunks
for group, group_chunks in chunks_dict.items():
for _, chunk in group_chunks.iterrows():
start_ts_id = f'ts{ts_id_counter}'
if chunk['start_time'] not in time_slot_map:
ET.SubElement(time_order, 'TIME_SLOT', {'TIME_SLOT_ID': start_ts_id, 'TIME_VALUE': str(int(chunk['start_time']))})
time_slot_map[chunk['start_time']] = start_ts_id
ts_id_counter += 1
else:
start_ts_id = time_slot_map[chunk['start_time']]
end_ts_id = f'ts{ts_id_counter}'
if chunk['end_time'] not in time_slot_map:
ET.SubElement(time_order, 'TIME_SLOT', {'TIME_SLOT_ID': end_ts_id, 'TIME_VALUE': str(int(chunk['end_time']))})
time_slot_map[chunk['end_time']] = end_ts_id
ts_id_counter += 1
else:
end_ts_id = time_slot_map[chunk['end_time']]
# Create the tiers
for group, group_chunks in chunks_dict.items():
tier = ET.SubElement(annotation_document, 'TIER', {
'LINGUISTIC_TYPE_REF': "mov_detect",
'TIER_ID': group
})
for i, chunk in group_chunks.iterrows():
start_ts_id = time_slot_map[chunk['start_time']]
end_ts_id = time_slot_map[chunk['end_time']]
annotation_id = f'{group}_a{i + 1}'
annotation = ET.SubElement(tier, 'ANNOTATION')
alignable_annotation = ET.SubElement(annotation, 'ALIGNABLE_ANNOTATION', {
'ANNOTATION_ID': annotation_id,
'TIME_SLOT_REF1': start_ts_id,
'TIME_SLOT_REF2': end_ts_id
})
ET.SubElement(alignable_annotation, 'ANNOTATION_VALUE').text = chunk['value']
# Add constraints and linguistic types
ET.SubElement(annotation_document, 'LINGUISTIC_TYPE', {
'GRAPHIC_REFERENCES': "false",
'LINGUISTIC_TYPE_ID': "mov_detect",
'TIME_ALIGNABLE': "true"
})
constraints = [
("Time subdivision of parent annotation's time interval, no time gaps allowed within this interval", "Time_Subdivision"),
("Symbolic subdivision of a parent annotation. Annotations referring to the same parent are ordered", "Symbolic_Subdivision"),
("1-1 association with a parent annotation", "Symbolic_Association"),
("Time alignable annotations within the parent annotation's time interval, gaps are allowed", "Included_In")
]
for desc, stereotype in constraints:
ET.SubElement(annotation_document, 'CONSTRAINT', {'DESCRIPTION': desc, 'STEREOTYPE': stereotype})
tree = ET.ElementTree(annotation_document)
tree.write(output_file, encoding='UTF-8', xml_declaration=True)
apply function¶
In [332]:
elanfiles = glob.glob(annofolder + '/*ELAN_tiers.csv')
for file in elanfiles:
print('working on ' + file)
# load the file
df = pd.read_csv(file)
# get chunks
parent_chunks = get_chunks(df, 'Time', 'movement_in_trial')
head_chunks = get_chunks(df, 'Time', 'head_mov')
upper_body_chunks = get_chunks(df, 'Time', 'upper_body')
arms_chunks = get_chunks(df, 'Time', 'arms')
lower_body_chunks = get_chunks(df, 'Time', 'lower_body')
# Adjust end time of each chunk to be the start time of the next chunk
parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
# chunk dictionary
chunks_dict = {
'movement_in_trial': parent_chunks,
'head_mov': head_chunks,
'upper_body': upper_body_chunks,
'arms': arms_chunks,
'lower_body': lower_body_chunks
}
# create the eaf file
create_eaf(chunks_dict, file.replace('.csv', '.eaf'))
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_0_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_10_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_11_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_12_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_13_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_14_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_15_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_16_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_17_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_18_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_19_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_1_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_20_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_21_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_22_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_23_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_24_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_25_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_26_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_27_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_28_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_29_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_2_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_30_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_31_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_32_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_33_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_35_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_36_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_37_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_38_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_39_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_3_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_40_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_41_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_42_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_43_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_44_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_45_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_46_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_47_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_48_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_49_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_4_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_50_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_51_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_52_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_53_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_5_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_6_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_7_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_8_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_9_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_tpose_0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_tpose_1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_0_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_100_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_101_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_102_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_103_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_104_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_105_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_106_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_107_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_108_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_109_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_10_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_110_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_111_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_112_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_113_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_11_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_12_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_13_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_14_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_15_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_16_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_17_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_18_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_19_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_1_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_20_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_21_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_22_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_23_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_24_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_25_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_26_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_27_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_28_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_29_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_2_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_30_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_31_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_32_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_33_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_34_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_35_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_36_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_37_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_38_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_39_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_3_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_40_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_41_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_43_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_44_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_45_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_46_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_47_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_48_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_49_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_4_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_50_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_51_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_52_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_53_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_54_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_55_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_56_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_57_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_58_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_59_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_5_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_60_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_61_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_62_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_63_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_64_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_65_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_67_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_68_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_69_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_6_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_70_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_71_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_72_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_73_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_74_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_75_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_76_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_77_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_78_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_79_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_7_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_80_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_81_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_82_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_83_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_84_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_85_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_86_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_87_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_88_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_89_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_8_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_90_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_91_p0_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_92_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_93_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_94_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_95_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_96_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_97_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:] C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_98_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_99_p1_ELAN_tiers.csv working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_9_p0_ELAN_tiers.csv
Editing eaf files for manual annotation¶
function¶
In [3]:
import xml.etree.ElementTree as ET
import glob
def add_nomovement_annotations(xml_file_path, newfilepath):
# Load the XML file
tree = ET.parse(xml_file_path)
root = tree.getroot()
# Extract all time slots
time_slots = {}
for time_slot in root.find('TIME_ORDER').findall('TIME_SLOT'):
time_slots[time_slot.attrib['TIME_SLOT_ID']] = int(time_slot.attrib['TIME_VALUE'])
# Sort time slots by TIME_VALUE
sorted_time_slots = sorted(time_slots.items(), key=lambda x: x[1])
time_slot_ids = [ts[0] for ts in sorted_time_slots]
time_values = [ts[1] for ts in sorted_time_slots]
# Loop over all tiers
for tier in root.findall('TIER'):
annotations = tier.findall('ANNOTATION/ALIGNABLE_ANNOTATION')
if not annotations:
# If no annotations exist, add a single 'nomovement' annotation covering the whole tier
new_annotation = ET.Element('ANNOTATION')
alignable_annotation = ET.SubElement(new_annotation, 'ALIGNABLE_ANNOTATION')
alignable_annotation.set('TIME_SLOT_REF1', time_slot_ids[0])
alignable_annotation.set('TIME_SLOT_REF2', time_slot_ids[-1])
annotation_value = ET.SubElement(alignable_annotation, 'ANNOTATION_VALUE')
annotation_value.text = 'nomovement'
tier.append(new_annotation)
else:
# Sort annotations by start time
sorted_annotations = sorted(annotations, key=lambda x: time_slots[x.attrib['TIME_SLOT_REF1']])
# Handle the first annotation
first_annotation = sorted_annotations[0]
first_start_time = time_slots[first_annotation.attrib['TIME_SLOT_REF1']]
if first_start_time > time_values[0]:
new_annotation = ET.Element('ANNOTATION')
alignable_annotation = ET.SubElement(new_annotation, 'ALIGNABLE_ANNOTATION')
alignable_annotation.set('TIME_SLOT_REF1', time_slot_ids[0])
alignable_annotation.set('TIME_SLOT_REF2', first_annotation.attrib['TIME_SLOT_REF1'])
annotation_value = ET.SubElement(alignable_annotation, 'ANNOTATION_VALUE')
annotation_value.text = 'nomovement'
tier.append(new_annotation)
# Handle gaps between annotations
for i in range(len(sorted_annotations) - 1):
current_annotation = sorted_annotations[i]
next_annotation = sorted_annotations[i + 1]
current_end_time = time_slots[current_annotation.attrib['TIME_SLOT_REF2']]
next_start_time = time_slots[next_annotation.attrib['TIME_SLOT_REF1']]
if current_end_time < next_start_time:
new_annotation = ET.Element('ANNOTATION')
alignable_annotation = ET.SubElement(new_annotation, 'ALIGNABLE_ANNOTATION')
alignable_annotation.set('TIME_SLOT_REF1', current_annotation.attrib['TIME_SLOT_REF2'])
alignable_annotation.set('TIME_SLOT_REF2', next_annotation.attrib['TIME_SLOT_REF1'])
annotation_value = ET.SubElement(alignable_annotation, 'ANNOTATION_VALUE')
annotation_value.text = 'nomovement'
tier.append(new_annotation)
# Handle the last annotation
last_annotation = sorted_annotations[-1]
last_end_time = time_slots[last_annotation.attrib['TIME_SLOT_REF2']]
if last_end_time < time_values[-1]:
new_annotation = ET.Element('ANNOTATION')
alignable_annotation = ET.SubElement(new_annotation, 'ALIGNABLE_ANNOTATION')
alignable_annotation.set('TIME_SLOT_REF1', last_annotation.attrib['TIME_SLOT_REF2'])
alignable_annotation.set('TIME_SLOT_REF2', time_slot_ids[-1])
annotation_value = ET.SubElement(alignable_annotation, 'ANNOTATION_VALUE')
annotation_value.text = 'nomovement'
tier.append(new_annotation)
# Save the modified XML file as a new file
tree.write(newfilepath, encoding='UTF-8', xml_declaration=True)
apply function¶
In [4]:
manualanno_folder_r1 = curfolder + '/ManualAnno/R1/' # ola
manualanno_folder_r3 = curfolder + '/ManualAnno/R3/' # gillian
manualannofiles1 = glob.glob(manualanno_folder_r1 + '/*.eaf')
manualannofiles3 = glob.glob(manualanno_folder_r3 + '/*.eaf')
for file in manualannofiles1:
print('working on ' + file)
# new filename is without third part of the name
newfile = file.split('\\')[-1]
chunks = newfile.split('_')
if 'corrected' in file:
if 'c0' in file or 'c1' in file or 'c2' in file:
newfile = '_'.join(chunks[:-4])
else:
newfile = '_'.join(chunks[:-3])
else:
if 'c0' in file or 'c1' in file or 'c2' in file:
newfile = '_'.join(chunks[:-3])
else:
newfile = '_'.join(chunks[:-2])
# replace trial_ with _
newfile = newfile.replace('trial_', '')
# add filepath
newfile = manualanno_folder_r1 + newfile + '_ELAN_tiers.eaf'
add_nomovement_annotations(file, newfile)
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_11_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_12_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_13_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_14_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_15_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_16_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_17_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_20_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_21_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_22_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_23_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_24_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_25_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_26_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_29_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_30_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_31_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_32_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_33_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_34_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_35_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_38_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_39_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_40_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_41_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_42_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_43_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_44_p0_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_47_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_48_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_49_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_50_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_51_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_52_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_53_p1_ELAN_tiers.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_11_p1_eten_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_12_p1_ei_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_13_p1_zwemmen_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_14_p1_snel_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_15_p1_regen_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_16_p1_boos_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_17_p1_luidruchtig_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_20_p0_verdrietig_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_21_p0_koud_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_22_p0_staan_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_23_p0_stil_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_24_p0_lopen_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_25_p0_bang_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_26_p0_gooien_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_29_p1_rennen_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_30_p1_likken_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_31_p1_klein_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_32_p1_onweer_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_33_p1_man_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_34_p1_springen_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_35_p1_langzaam_combinatie.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_38_p0_kind_gebaren.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_39_p0_hoorn_gebaren.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_40_p0_kotsen_gebaren.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_41_p0_dood_gebaren.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_42_p0_drinken_gebaren.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_43_p0_sterk_gebaren.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_44_p0_oud_gebaren.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_47_p1_slang_gebaren.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_48_p1_zuur_gebaren.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_49_p1_niet_gebaren.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_50_p1_weten_gebaren.eaf working on 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e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_90_p0_jagen_geluiden_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_91_p0_jagen_geluiden_c2.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_98_p1_zwaar_geluiden_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_99_p1_zwaar_geluiden_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_100_p1_zwaar_geluiden_c2.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_101_p1_grommen_geluiden_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_102_p1_zout_geluiden_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_103_p1_zout_geluiden_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_104_p1_zout_geluiden_c2.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_105_p1_zuigen_geluiden_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_106_p1_zuigen_geluiden_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_107_p1_zuigen_geluiden_c2.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_108_p1_dik_geluiden_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_109_p1_dik_geluiden_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_110_p1_dik_geluiden_c2.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_111_p1_ziek_geluiden_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_112_p1_ziek_geluiden_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_113_p1_huilen_geluiden_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_2_p0_bitter_geluiden_corrected.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_3_p0_vechten_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_4_p0_ademen_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_5_p0_bijten_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_6_p0_zoemen_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_7_p0_fluisteren_geluiden.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_8_p0_walgen_geluiden_corrected.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_2_p0_vrouw_combinatie_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_3_p0_vrouw_combinatie_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_4_p0_vrouw_combinatie_c2.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_5_p0_verbranden_combinatie_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_6_p0_verbranden_combinatie_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_7_p0_verbranden_combinatie_c2.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_8_p0_ik_combinatie_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_9_p0_kauwen_combinatie_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_10_p0_vliegen_combinatie_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_11_p0_vliegen_combinatie_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_12_p0_vliegen_combinatie_c2.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_13_p0_misschien_combinatie_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_14_p0_misschien_combinatie_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_15_p0_misschien_combinatie_c2.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_16_p0_bliksem_combinatie_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_17_p0_bliksem_combinatie_c1.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_18_p0_bliksem_combinatie_c2.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_23_p1_groot_combinatie_c0.eaf working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_24_p1_groot_combinatie_c1.eaf working on 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e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_74_p0_geur_geluiden_c1.eaf
Text files for interrater agreement¶
In [10]:
import xml.etree.ElementTree as ET
def parse_eaf_file(eaf_file, rel_tiers):
tree = ET.parse(eaf_file)
root = tree.getroot()
time_order = root.find('TIME_ORDER')
time_slots = {time_slot.attrib['TIME_SLOT_ID']: time_slot.attrib['TIME_VALUE'] for time_slot in time_order}
annotations = []
relevant_tiers = {rel_tiers}
for tier in root.findall('TIER'):
tier_id = tier.attrib['TIER_ID']
if tier_id in relevant_tiers:
for annotation in tier.findall('ANNOTATION/ALIGNABLE_ANNOTATION'):
print(annotation)
# Ensure required attributes are present
if 'TIME_SLOT_REF1' in annotation.attrib and 'TIME_SLOT_REF2' in annotation.attrib:
ts_ref1 = annotation.attrib['TIME_SLOT_REF1']
ts_ref2 = annotation.attrib['TIME_SLOT_REF2']
# Get annotation ID if it exists, otherwise set to None
ann_id = annotation.attrib.get('ANNOTATION_ID', None)
annotation_value = annotation.find('ANNOTATION_VALUE').text.strip()
annotations.append({
'tier_id': tier_id,
'annotation_id': ann_id,
'start_time': time_slots[ts_ref1],
'end_time': time_slots[ts_ref2],
'annotation_value': annotation_value
})
return annotations
Folder setting¶
In [4]:
annofolder = curfolder + '/MT_annotated/'
autoannofiles = glob.glob(annofolder + '/*ELAN_tiers.eaf')
interfolder = curfolder + '/InterAg/'
print(autoannofiles)
manualannofiles_r1 = glob.glob(curfolder + '/ManualAnno/R1/*ELAN_tiers.eaf') # ola
manualannofiles_r3 = glob.glob(curfolder + '/ManualAnno/R3/*ELAN_tiers.eaf') # gillian
['e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_0_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_11_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_12_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_13_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_15_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_2_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_39_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_3_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_49_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_4_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_6_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_8_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_9_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_tpose_0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_tpose_1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_19_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_30_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_70_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_71_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_72_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_73_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_74_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_77_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_7_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_80_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_82_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_85_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_86_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_87_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_88_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_8_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_9_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_10_p1_ELAN_tiers.eaf', 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'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_25_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_26_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_27_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_28_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_29_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_30_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_31_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_32_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_33_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_35_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_36_p0_ELAN_tiers.eaf', 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'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_50_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_51_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_52_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_53_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_5_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_7_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_0_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_100_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_101_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_102_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_103_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_104_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_105_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_106_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_107_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_108_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_109_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_10_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_110_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_111_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_112_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_113_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_11_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_12_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_13_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_14_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_15_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_16_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_17_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_18_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_1_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_20_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_21_p1_ELAN_tiers.eaf', 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TXT files, automatic anno¶
In [333]:
# empty txtfiles
txtfile_auto_mov = interfolder + 'AutoAnno_mov.txt'
txtfile_auto_head = interfolder + 'AutoAnno_head.txt'
txtfile_auto_upper = interfolder + 'AutoAnno_upper.txt'
txtfile_auto_arms = interfolder + 'AutoAnno_arms.txt'
txtfile_auto_lower = interfolder + 'AutoAnno_lower.txt'
with open(txtfile_auto_mov, 'w') as f:
for file in autoannofiles:
print('working on ' + file)
filename = file.split('\\')[-1]
annotator = 'Anno_R2'
annotations = parse_eaf_file(file, 'movement_in_trial')
for annotation in annotations:
print(annotation)
f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
with open(txtfile_auto_head, 'w') as f:
for file in autoannofiles:
print('working on ' + file)
filename = file.split('\\')[-1]
annotator = 'Anno_R2'
annotations = parse_eaf_file(file, 'head_mov')
for annotation in annotations:
f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
with open(txtfile_auto_upper, 'w') as f:
for file in autoannofiles:
print('working on ' + file)
filename = file.split('\\')[-1]
annotator = 'Anno_R2'
annotations = parse_eaf_file(file, 'upper_body')
for annotation in annotations:
f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
with open(txtfile_auto_arms, 'w') as f:
for file in autoannofiles:
print('working on ' + file)
filename = file.split('\\')[-1]
annotator = 'Anno_R2'
annotations = parse_eaf_file(file, 'arms')
for annotation in annotations:
f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
with open(txtfile_auto_lower, 'w') as f:
for file in autoannofiles:
print('working on ' + file)
filename = file.split('\\')[-1]
annotator = 'Anno_R2'
annotations = parse_eaf_file(file, 'lower_body')
for annotation in annotations:
f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_0_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_11_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_12_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_13_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_15_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_39_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_36_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_37_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_38_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_40_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_45_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_46_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_47_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_48_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_50_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_51_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_52_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_53_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_7_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_0_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_100_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_101_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_102_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_103_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_104_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_105_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_106_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_107_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_108_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_109_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_110_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_111_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_112_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_113_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_11_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_12_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_13_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_14_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_15_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_16_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_17_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_18_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_1_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_20_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_21_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_22_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_23_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_24_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_25_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_26_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_27_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_28_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_36_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_37_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_38_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_39_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_40_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_45_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_46_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_47_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_48_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_49_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_4_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_50_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_51_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_52_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_53_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_54_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_55_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_56_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_57_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_58_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_59_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_60_p1_ELAN_tiers.eaf
{'tier_id': 'movement_in_trial', 'annotation_id': 'movement_in_trial_a1', 'start_time': '0', 'end_time': '1016', 'annotation_value': 'nomovement'}
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_61_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_62_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_63_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_64_p1_ELAN_tiers.eaf
{'tier_id': 'movement_in_trial', 'annotation_id': 'movement_in_trial_a1', 'start_time': '0', 'end_time': '2349', 'annotation_value': 'nomovement'}
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_65_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_67_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_68_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_69_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_6_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_75_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_76_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_78_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_79_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_81_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_83_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_84_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_89_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_91_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_93_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_94_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_95_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_98_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_60_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_61_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_62_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_63_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_64_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_65_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_67_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_68_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_69_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_6_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_75_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_76_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_78_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_79_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_81_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_83_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_84_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_89_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_90_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_91_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_92_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_93_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_94_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_95_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_96_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_97_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_98_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_99_p1_ELAN_tiers.eaf
TXT files, manual anno¶
In [11]:
# define which folder you want to use
foi = manualannofiles_r3
raterIDfile = 'R3'
raterID = 'R2'
# IA files
txtfile_mov = interfolder + raterIDfile + '_Manual_mov.txt'
txtfile_head = interfolder + raterIDfile + '_Manual_head.txt'
txtfile_upper = interfolder + raterIDfile + '_Manual_upper.txt'
txtfile_lower = interfolder + raterIDfile + '_Manual_lower.txt'
txtfile_arms = interfolder + raterIDfile + '_Manual_arms.txt'
with open(txtfile_mov, 'w') as f:
for file in foi:
print('working on ' + file)
# get the filename as the last element
filename = file.split('\\')[-1]
annotator = 'Anno_' + raterID # no matter what ID, for easydiag it always has to be agreement between R1 and R2
# parse the file
annotations = parse_eaf_file(file, 'movement_in_trial')
print(annotations)
# write the annotations
for annotation in annotations:
f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
with open(txtfile_head, 'w') as f:
for file in foi:
print('working on ' + file)
# get the filename as the last element
filename = file.split('\\')[-1]
# parse the file
annotations = parse_eaf_file(file, 'head_mov')
print(annotations)
# write the annotations
for annotation in annotations:
f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
with open(txtfile_upper, 'w') as f:
for file in foi:
print('working on ' + file)
# get the filename as the last element
filename = file.split('\\')[-1]
annotator = 'Anno_' + raterID
# parse the file
annotations = parse_eaf_file(file, 'upper_body')
print(annotations)
# write the annotations
for annotation in annotations:
f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
with open(txtfile_lower, 'w') as f:
for file in foi:
print('working on ' + file)
# get the filename as the last element
filename = file.split('\\')[-1]
annotator = 'Anno_' + raterID
# parse the file
annotations = parse_eaf_file(file, 'lower_body')
print(annotations)
# write the annotations
for annotation in annotations:
f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
with open(txtfile_arms, 'w') as f:
for file in foi:
print('working on ' + file)
# get the filename as the last element
filename = file.split('\\')[-1]
annotator = 'Anno_' + raterID
# parse the file
annotations = parse_eaf_file(file, 'arms')
print(annotations)
# write the annotations
for annotation in annotations:
f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_11_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3380>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '3116', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_12_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1080>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '3629', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_13_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3380>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '3388', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_14_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1080>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '5120', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_15_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3380>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '3978', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_16_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1080>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '3524', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_17_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A33D0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1170>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8B80>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a5', 'start_time': '1560', 'end_time': '3310', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '1560', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3310', 'end_time': '4263', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_20_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A0C20>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33E06D9E0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8900>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '620', 'end_time': '3760', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '620', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3760', 'end_time': '3881', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_21_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A32E0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8900>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8950>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '610', 'end_time': '3514', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '610', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3514', 'end_time': '3595', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_22_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A0D10>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1120>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1CB0>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a2', 'start_time': '540', 'end_time': '4499', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '540', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '4499', 'end_time': '4575', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_23_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A36A0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8D10>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8540>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '980', 'end_time': '3734', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '980', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3734', 'end_time': '3844', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_24_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3790>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A32E0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3650>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a2', 'start_time': '490', 'end_time': '5558', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '490', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '5558', 'end_time': '5699', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_25_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1170>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B84F0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8B30>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '650', 'end_time': '6089', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '650', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '6089', 'end_time': '6158', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_26_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A33D0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A36A0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3650>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a2', 'start_time': '920', 'end_time': '3890', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '920', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3890', 'end_time': '4138', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_29_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8450>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9080>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B84A0>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '420', 'end_time': '3859', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '420', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3859', 'end_time': '4350', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_2_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A0D10>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '2776', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_30_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A32E0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A0BD0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8360>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '570', 'end_time': '3319', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '570', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3319', 'end_time': '3763', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_31_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3510>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B84F0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B93F0>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '550', 'end_time': '3399', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '550', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3399', 'end_time': '3832', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_32_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9440>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9670>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8310>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '1510', 'end_time': '4619', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '1510', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '4619', 'end_time': '4970', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_33_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A35B0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9210>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8DB0>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '410', 'end_time': '5079', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '410', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '5079', 'end_time': '5547', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_34_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8DB0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8770>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8BD0>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '380', 'end_time': '3460', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '380', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3460', 'end_time': '4012', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_35_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8B30>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8450>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9080>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '590', 'end_time': '4779', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '590', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '4779', 'end_time': '5167', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_38_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A0BD0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A31F0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A31A0>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a2', 'start_time': '800', 'end_time': '5070', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '800', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '5070', 'end_time': '5366', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_39_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A10D0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8630>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8FE0>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '1400', 'end_time': '6710', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '1400', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '6710', 'end_time': '7114', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_3_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A0EF0>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '8888', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_40_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B87C0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B96C0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8360>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '560', 'end_time': '2390', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '560', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '2390', 'end_time': '3026', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_41_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A36A0>
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_90_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_91_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_98_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_99_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_9_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_11_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_12_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_13_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_14_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_15_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_16_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_17_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_20_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_21_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_22_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_23_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_24_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_25_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_26_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_38_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_39_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_40_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_47_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_48_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_49_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_4_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_50_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_51_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_52_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_53_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_6_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_7_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_8_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_100_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_101_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_102_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_103_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_104_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_105_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_106_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_107_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_108_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_109_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_10_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_110_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_111_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_112_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_113_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_11_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_12_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_13_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_14_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_15_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_16_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_17_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_18_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_23_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_24_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_25_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_26_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_27_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_28_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_36_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_37_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_45_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_46_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_47_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_48_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_49_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_4_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_50_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_51_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_52_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_57_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_58_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_59_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_60_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_61_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_62_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_63_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_64_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_65_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_66_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_6_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_73_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_74_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_75_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_76_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_77_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_78_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_79_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_7_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_80_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_81_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_82_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_83_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_84_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_85_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_86_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_87_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_88_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_89_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_8_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_90_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_91_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_98_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_99_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_9_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_11_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_12_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_13_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_14_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_15_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_16_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_17_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_20_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_21_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_22_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_23_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_24_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_47_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_49_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_51_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_52_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_53_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_7_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_101_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_102_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_103_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_105_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_106_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_107_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_108_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_12_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_13_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_14_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_15_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_16_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_17_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_18_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_23_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_24_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_25_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_26_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_27_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_28_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_36_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_37_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_81_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_87_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_89_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_49_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_51_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_8_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_91_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_98_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_99_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_9_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_11_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_13_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_15_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_16_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_17_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_20_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_21_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_22_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_23_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_24_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_25_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_26_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_38_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_39_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_40_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_47_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_48_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_49_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_52_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_6_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_7_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_8_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_100_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_101_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_102_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_103_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_104_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_105_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_106_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_107_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_108_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_109_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_10_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_110_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_112_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_113_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_11_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_12_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_13_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_14_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_16_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_17_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_18_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_23_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_25_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_26_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_27_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_28_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_45_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_46_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_47_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_48_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_49_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_50_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_51_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_52_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_57_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_58_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_59_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_60_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_61_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_62_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_63_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_64_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_65_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_66_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_6_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_73_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_74_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_75_p0_ELAN_tiers.eaf
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[{'tier_id': 'arms', 'annotation_id': 'a7', 'start_time': '450', 'end_time': '5240', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '450', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '5240', 'end_time': '5326', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_76_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9D50>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3729', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_77_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B89F0>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '4539', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_78_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B99E0>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '4288', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_79_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9A80>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9350>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9F30>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '320', 'end_time': '4160', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '320', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '4160', 'end_time': '4226', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_7_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9A80>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9C60>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8EF0>
[{'tier_id': 'arms', 'annotation_id': 'a6', 'start_time': '1440', 'end_time': '7120', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '1440', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '7120', 'end_time': '7873', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_80_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9850>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3400', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_81_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9D00>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B93F0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B95D0>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '250', 'end_time': '2339', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '250', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '2339', 'end_time': '4132', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_82_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9EE0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9BC0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B93F0>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '1120', 'end_time': '4329', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '1120', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '4329', 'end_time': '5280', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_83_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8AE0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8630>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8E00>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '1629', 'end_time': '4869', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '1629', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '4869', 'end_time': '5548', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_84_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8630>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8AE0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B90D0>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '1640', 'end_time': '4880', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '1640', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '4880', 'end_time': '5045', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_85_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8C70>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '4142', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_86_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9300>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3705', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_87_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8D60>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3689', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_88_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9F80>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '2928', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_89_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8D60>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B86D0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9DF0>
[{'tier_id': 'arms', 'annotation_id': 'a6', 'start_time': '3080', 'end_time': '5280', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3080', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '5280', 'end_time': '5549', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_8_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8810>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9710>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B89A0>
[{'tier_id': 'arms', 'annotation_id': 'a4', 'start_time': '250', 'end_time': '2290', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '250', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '2290', 'end_time': '3148', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_90_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9170>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8590>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6BA020>
[{'tier_id': 'arms', 'annotation_id': 'a6', 'start_time': '570', 'end_time': '2929', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '570', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '2929', 'end_time': '3909', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_91_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9BC0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9C60>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8810>
[{'tier_id': 'arms', 'annotation_id': 'a6', 'start_time': '840', 'end_time': '2590', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '840', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '2590', 'end_time': '3149', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_98_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8D60>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '5395', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_99_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9BC0>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '6185', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_9_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B89F0>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3890', 'annotation_value': 'nomovement'}]
Merge txt files for EasyDIAG¶
In [12]:
# open AutoAnno_arms.txt and ManualAnno_arms.txt
# check which files are in the ManualAnno_arms.txt but not in AutoAnno_arms.txt
# get rows that contains these files from both txt files into one txt file IA_arms
toi = ['arms', 'head', 'upper', 'lower', 'mov']
for tier in toi:
print('working on ' + tier)
txtfile_auto = interfolder + 'AutoAnno_' + tier + '.txt'
txtfile_manual_r1 = interfolder + 'R1_Manual_' + tier + '.txt'
txtfile_manual_r3 = interfolder + 'R3_Manual_' + tier + '.txt'
comp1 = 'R1' # change here who you want to compare
comp2 = 'R3' # with whom
#read the files
r1_anno = pd.read_csv(txtfile_manual_r1, sep='\t', header=None) # change here who you want to compare
r2_anno = pd.read_csv(txtfile_manual_r3, sep='\t', header=None) # with whom
#get the files that are in manual_arms but not in auto_arms
files_to_check_r1 = r1_anno[4].unique()
files_to_check_r2 = r2_anno[4].unique()
# create a list that contains files that are in both lists
files_to_check = list(set(files_to_check_r1) & set(files_to_check_r2))
# put away those that have 0_1_34, 0_2_42, and 0_2_66 in them - these are faulty trials in pose2sim so we dont have auto anno for them
files_to_check = [x for x in files_to_check if '0_1_34' not in x and '0_2_42' not in x and '0_2_66' not in x]
# get the rows that contain these files from auto_arms
rows_auto = r1_anno[r1_anno[4].isin(files_to_check)]
rows_manual = r2_anno[r2_anno[4].isin(files_to_check)]
#concat rows_to_check_auto with manual_arms
concat_rows = pd.concat([rows_auto, rows_manual])
#save the rows to a new txt file
txtfile_IA_arms = interfolder + 'IA_' + comp1 + '_' + comp2 + '_' + tier + '.txt'
with open(txtfile_IA_arms, 'w') as f:
for index, row in concat_rows.iterrows():
f.write(f"{row[0]}\t{row[1]}\t{row[2]}\t{row[3]}\t{row[4]}\n")
working on arms working on head working on upper working on lower working on mov